diff --git "a/notebooks/E04-forecaster.ipynb" "b/notebooks/E04-forecaster.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebooks/E04-forecaster.ipynb"
@@ -0,0 +1,1509 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# [Load packages]\n",
+ "\n",
+ "# To call functions outside of this folder\n",
+ "import sys \n",
+ "sys.path.insert(0, '..')\n",
+ "\n",
+ "import pandas as pd\n",
+ "from src.forecaster import Forecaster\n",
+ "from src.analyser import Analyser\n",
+ "import logging\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Load data and fit forecaster"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Filtered out only y and gas_regm columns\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " y | \n",
+ " gas_regm | \n",
+ "
\n",
+ " \n",
+ " datetime | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 2000-01-31 | \n",
+ " 41.0 | \n",
+ " 1.289 | \n",
+ "
\n",
+ " \n",
+ " 2000-02-29 | \n",
+ " 41.0 | \n",
+ " 1.377 | \n",
+ "
\n",
+ " \n",
+ " 2000-03-31 | \n",
+ " 45.0 | \n",
+ " 1.516 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " y gas_regm\n",
+ "datetime \n",
+ "2000-01-31 41.0 1.289\n",
+ "2000-02-29 41.0 1.377\n",
+ "2000-03-31 45.0 1.516"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# [Load data]\n",
+ "data = pd.read_csv(\n",
+ " '../data/multivariate/blow_mold_preprocessed.csv', \n",
+ " index_col='datetime', \n",
+ " parse_dates=['datetime'])\n",
+ "\n",
+ "print('Filtered out only y and gas_regm columns')\n",
+ "data = data[['y', 'gas_regm']]\n",
+ "\n",
+ "data.head(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Forecaster - fit] ----- START -----\n",
+ "[Forecaster - fit] Train test split\n",
+ "[Forecaster - fit] Test size: 12\n",
+ "[Forecaster - fit] Sliding window splitter, with window_length 276 and fh 12\n",
+ "[Forecaster - fit] ----- END -----\n"
+ ]
+ }
+ ],
+ "source": [
+ "# [Init forecaster]\n",
+ "\n",
+ "window_length = 12 # Auto correlation, lags\n",
+ "n_predict = 12 # Forecast horizon\n",
+ "\n",
+ "forecaster = Forecaster()\n",
+ "forecaster.fit(data, n_predict=n_predict, window_length=window_length)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " y | \n",
+ " gas_regm | \n",
+ "
\n",
+ " \n",
+ " datetime | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 2000-01-31 | \n",
+ " 41.0 | \n",
+ " 1.289 | \n",
+ "
\n",
+ " \n",
+ " 2000-02-29 | \n",
+ " 41.0 | \n",
+ " 1.377 | \n",
+ "
\n",
+ " \n",
+ " 2000-03-31 | \n",
+ " 45.0 | \n",
+ " 1.516 | \n",
+ "
\n",
+ " \n",
+ " 2000-04-30 | \n",
+ " 47.0 | \n",
+ " 1.465 | \n",
+ "
\n",
+ " \n",
+ " 2000-05-31 | \n",
+ " 47.0 | \n",
+ " 1.487 | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 2022-08-31 | \n",
+ " 93.0 | \n",
+ " 3.975 | \n",
+ "
\n",
+ " \n",
+ " 2022-09-30 | \n",
+ " 90.0 | \n",
+ " 3.700 | \n",
+ "
\n",
+ " \n",
+ " 2022-10-31 | \n",
+ " 90.0 | \n",
+ " 3.815 | \n",
+ "
\n",
+ " \n",
+ " 2022-11-30 | \n",
+ " 90.0 | \n",
+ " 3.685 | \n",
+ "
\n",
+ " \n",
+ " 2022-12-31 | \n",
+ " 90.0 | \n",
+ " 3.210 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
276 rows × 2 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " y gas_regm\n",
+ "datetime \n",
+ "2000-01-31 41.0 1.289\n",
+ "2000-02-29 41.0 1.377\n",
+ "2000-03-31 45.0 1.516\n",
+ "2000-04-30 47.0 1.465\n",
+ "2000-05-31 47.0 1.487\n",
+ "... ... ...\n",
+ "2022-08-31 93.0 3.975\n",
+ "2022-09-30 90.0 3.700\n",
+ "2022-10-31 90.0 3.815\n",
+ "2022-11-30 90.0 3.685\n",
+ "2022-12-31 90.0 3.210\n",
+ "\n",
+ "[276 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "forecaster.data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# [Build exogenous data for testing, with weekday and datetime column]\n",
+ "\n",
+ "exog_index = pd.date_range(\"2000-01-31\", \"2023-12-31\", freq='M')\n",
+ "exog_weekday = exog_index.weekday\n",
+ "exog = pd.DataFrame({'weekday':exog_weekday}, index=exog_index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " weekday | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 2000-01-31 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 2000-02-29 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 2000-03-31 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " 2000-04-30 | \n",
+ " 6 | \n",
+ "
\n",
+ " \n",
+ " 2000-05-31 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 2023-08-31 | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " 2023-09-30 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 2023-10-31 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 2023-11-30 | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " 2023-12-31 | \n",
+ " 6 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
288 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " weekday\n",
+ "2000-01-31 0\n",
+ "2000-02-29 1\n",
+ "2000-03-31 4\n",
+ "2000-04-30 6\n",
+ "2000-05-31 2\n",
+ "... ...\n",
+ "2023-08-31 3\n",
+ "2023-09-30 5\n",
+ "2023-10-31 1\n",
+ "2023-11-30 3\n",
+ "2023-12-31 6\n",
+ "\n",
+ "[288 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "exog"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[Forecaster - fit] ----- START -----\n",
+ "[Forecaster - fit] - exogenous data provided\n",
+ "[Forecaster - fit] - merge exogenous data with features\n",
+ "[Forecaster - fit] Train test split\n",
+ "[Forecaster - fit] Test size: 12\n",
+ "[Forecaster - fit] Sliding window splitter, with window_length 276 and fh 12\n",
+ "[Forecaster - fit] ----- END -----\n"
+ ]
+ }
+ ],
+ "source": [
+ "# [Fit forecaster with new exog column]\n",
+ "\n",
+ "forecaster.fit(data, n_predict=n_predict, window_length=window_length, exog=exog)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data.merge(exog, left_index=True, right_index=True).reset_index().rename(columns={'index':'datetime'}).to_csv('../data/multivariate/demo_historical.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "exog[-12:-8].reset_index().rename(columns={'index':'datetime'}).to_csv('../data/multivariate/demo_future.csv', index=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "DatetimeIndex(['2023-01-31', '2023-02-28', '2023-03-31', '2023-04-30',\n",
+ " '2023-05-31', '2023-06-30', '2023-07-31', '2023-08-31',\n",
+ " '2023-09-30', '2023-10-31', '2023-11-30', '2023-12-31'],\n",
+ " dtype='datetime64[ns]', freq='M')"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "forecaster.fh.to_pandas()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Init model ...\n",
+ "Init model ...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/zq/miniconda3/lib/python3.10/site-packages/sktime/forecasting/model_selection/_tune.py:198: UserWarning: in ForecastingRandomizedSearchCV, n_jobs and pre_dispatch parameters are deprecated and will be removed in 0.26.0. Please use n_jobs and pre_dispatch directly in the backend_params argument instead.\n",
+ " warn(\n",
+ "/home/zq/miniconda3/lib/python3.10/site-packages/sktime/forecasting/model_selection/_tune.py:198: UserWarning: in ForecastingRandomizedSearchCV, n_jobs and pre_dispatch parameters are deprecated and will be removed in 0.26.0. Please use n_jobs and pre_dispatch directly in the backend_params argument instead.\n",
+ " warn(\n",
+ "/home/zq/.local/lib/python3.10/site-packages/sklearn/model_selection/_search.py:305: UserWarning: The total space of parameters 70 is smaller than n_iter=100. Running 70 iterations. For exhaustive searches, use GridSearchCV.\n",
+ " warnings.warn(\n",
+ "/home/zq/.local/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "/home/zq/.local/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "/home/zq/.local/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "/home/zq/.local/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "/home/zq/.local/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "/home/zq/.local/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "/home/zq/.local/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "/home/zq/.local/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "/home/zq/.local/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n",
+ "/home/zq/.local/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[{'model': 'xgbreg',\n",
+ " 'results': {'forecast': datetime\n",
+ " 2022-09-30 84.053032\n",
+ " 2022-10-31 68.322166\n",
+ " 2022-11-30 66.457939\n",
+ " 2022-12-31 66.370132\n",
+ " Freq: M, Name: y, dtype: float64,\n",
+ " 'best_score': 0.02539743402952789,\n",
+ " 'best_params': {'estimator__subsample': 0.5,\n",
+ " 'estimator__n_estimators': 500,\n",
+ " 'estimator__max_depth': 15,\n",
+ " 'estimator__learning_rate': 0.2,\n",
+ " 'estimator__colsample_bytree': 0.7999999999999999,\n",
+ " 'estimator__colsample_bylevel': 0.4}}},\n",
+ " {'model': 'mlr',\n",
+ " 'results': {'forecast': datetime\n",
+ " 2022-09-30 88.663381\n",
+ " 2022-10-31 84.754162\n",
+ " 2022-11-30 82.534963\n",
+ " 2022-12-31 81.363169\n",
+ " Freq: M, Name: y, dtype: float64,\n",
+ " 'best_score': 0.12320369211901486,\n",
+ " 'best_params': {'estimator__solver': 'sparse_cg',\n",
+ " 'estimator__fit_intercept': False,\n",
+ " 'estimator__alpha': 10}}}]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# [Predict without exog golumns]\n",
+ "\n",
+ "forecaster.forecast(test=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "