{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Models Examples\n", "\n", "First we must do some setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/science/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", " from pandas import MultiIndex, Int64Index\n" ] } ], "source": [ "import chimeric_tools.Simulation as ctc\n", "import chimeric_tools.Data as ctd\n", "import chimeric_tools.models as ctm\n", "from datetime import date\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from matplotlib.lines import Line2D\n", "import seaborn as sns\n", "plt.rc(\"figure\", figsize=(16, 6))\n", "plt.rc(\"savefig\", dpi=90)\n", "plt.rc(\"font\", family=\"sans-serif\")\n", "plt.rc(\"font\", size=14)\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training Simulated Data\n", "\n", "This is the only example you will need in this module. We are going to use simulated data and make a forecasts 1, 2, 3, and 4 weeks ahead for each week starting 15 weeks after the `start_date`." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 29] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 30] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 31] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 32] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 33] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 34] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 35] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 36] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 37] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 38] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 39] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 40] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 41] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 42] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 43] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 44] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:26 \n", "Last fit date: 2022-09-01 12:54:26 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 45] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 46] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 47] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 48] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 49] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 50] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 51] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 52] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 53] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 54] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 55] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 56] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 57] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 58] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 59] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 60] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 61] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 62] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 63] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 64] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 65] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 66] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 67] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 68] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 69] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 70] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 71] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 72] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 73] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 74] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 75] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 76] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 77] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:27 \n", "Last fit date: 2022-09-01 12:54:27 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 78] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:28 \n", "Last fit date: 2022-09-01 12:54:28 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 79] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:28 \n", "Last fit date: 2022-09-01 12:54:28 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 29] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 30] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 31] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 32] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 33] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 34] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 35] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 36] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 37] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 38] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 39] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 40] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 41] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 42] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 43] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 44] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 45] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 46] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 47] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 48] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 49] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 50] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 51] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 52] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 53] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 54] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 55] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 56] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 57] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 58] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 59] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 60] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 61] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 62] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 63] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 64] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 65] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:31 \n", "Last fit date: 2022-09-01 12:54:31 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 66] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 67] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 68] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 69] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 70] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 71] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 72] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 73] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 74] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 75] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 76] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 77] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 78] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 79] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:32 \n", "Last fit date: 2022-09-01 12:54:32 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 29] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 30] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 31] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 32] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 33] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 34] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 35] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 36] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 37] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 38] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 39] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 40] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 41] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 42] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:35 \n", "Last fit date: 2022-09-01 12:54:35 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 43] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 44] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 45] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 46] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 47] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 48] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 49] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 50] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 51] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 52] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 53] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 54] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 55] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 56] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 57] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 58] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 59] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 60] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 61] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 62] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 63] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 64] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 65] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 66] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 67] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 68] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 69] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 70] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 71] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 72] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 73] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 74] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 75] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 76] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 77] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:36 \n", "Last fit date: 2022-09-01 12:54:36 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 78] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:37 \n", "Last fit date: 2022-09-01 12:54:37 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 79] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:37 \n", "Last fit date: 2022-09-01 12:54:37 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 29] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 30] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 31] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 32] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 33] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 34] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 35] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 36] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 37] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 38] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 39] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 40] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 41] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 42] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 43] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 44] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 45] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 46] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 47] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 48] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 49] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 50] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 51] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 52] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 53] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 54] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 55] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:40 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 56] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:40 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 57] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 58] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 59] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 60] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 61] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 62] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 63] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 64] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 65] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 66] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 67] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 68] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 69] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 70] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 71] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 72] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 73] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 74] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 75] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 76] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 77] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 78] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 79] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:41 \n", "Last fit date: 2022-09-01 12:54:41 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 29] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 30] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 31] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 32] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 33] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 34] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 35] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 36] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 37] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 38] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 39] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 40] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 41] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 42] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 43] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 44] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 45] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 46] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 47] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 48] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 49] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 50] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 51] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 52] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 53] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 54] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 55] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 56] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 57] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 58] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:45 \n", "Last fit date: 2022-09-01 12:54:45 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 59] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 60] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 61] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 62] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 63] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 64] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 65] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 66] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 67] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 68] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 69] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 70] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 71] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 72] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 73] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 74] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 75] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 76] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 77] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 78] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n", "================= \n", "ForecasterAutoreg \n", "================= \n", "Regressor: Ridge() \n", "Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15] \n", "Window size: 15 \n", "Included exogenous: False \n", "Type of exogenous variable: None \n", "Exogenous variables names: None \n", "Training range: [0, 79] \n", "Training index type: RangeIndex \n", "Training index frequency: 1 \n", "Regressor parameters: {'alpha': 1.0, 'copy_X': True, 'fit_intercept': True, 'max_iter': None, 'normalize': 'deprecated', 'positive': False, 'random_state': None, 'solver': 'auto', 'tol': 0.001} \n", "Creation date: 2022-09-01 12:54:46 \n", "Last fit date: 2022-09-01 12:54:46 \n", "Skforecast version: 0.4.3 \n", "\n" ] } ], "source": [ "bs = ctc.COVID(start_date=\"2020-01-01\", end_date=\"2021-07-31\", geo_values=[\"US\", \"42095\"], include=[\"cases\"]).simulate(\"auto\", 5)\n", "preds = ctm.train_simulated_data(bs, [\"AR3\", \"AR6\", \"ridge\"], target=\"cases\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plotting the data\n", "\n", "Now it is time to plot the data to show what has actually happened under the hood" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mask = (preds.sim == 0) & (preds.forecast_date == date(2021, 7, 25)) & (preds.model == \"AR6\")\n", "sub_data = bs.loc[bs.sim == 0]\n", "sub_preds = preds.loc[mask]\n", "ctm.plot_single_predictions(sub_data, sub_preds, \"cases\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "mask = (preds.sim == 0) & (preds.forecast_date == date(2021, 7, 25)) & (preds.model == \"ridge\")\n", "sub_data = bs.loc[bs.sim == 0]\n", "sub_preds = preds.loc[mask]\n", "ctm.plot_single_predictions(sub_data, sub_preds, \"cases\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.9.12 ('science')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "f481d707f485e5e545fcbec8e23552587d82164162b1e7024dc6dabd3ca7d616" } } }, "nbformat": 4, "nbformat_minor": 2 }