.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "_examples\forecasting\plot_sarimax_search.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr__examples_forecasting_plot_sarimax_search.py: SARIMAX - Search ----------------------------- See `SARIMAX `_ .. GENERATED FROM PYTHON SOURCE LINES 8-125 .. image-sg:: /_examples/forecasting/images/sphx_glr_plot_sarimax_search_001.png :alt: Non-dynamic predictions for SARIMAX :srcset: /_examples/forecasting/images/sphx_glr_plot_sarimax_search_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Summary: sarimax-order sarimax-seasonal_order sarimax-trend sarimax-aic sarimax-bic 0 (0, 1, 1) (0, 0, 0, 12) c 769.3258 776.4342 1 (0, 1, 1) (0, 0, 0, 12) ct 767.6064 777.0842 2 (0, 1, 1) (0, 0, 0, 12) n 772.6176 777.3564 3 (1, 1, 0) (0, 0, 0, 12) n 773.3553 778.0942 4 (1, 1, 0) (0, 0, 0, 12) c 771.3858 778.4942 .. ... ... ... ... ... 287 (0, 0, 0) (0, 0, 0, 12) c 1018.8423 1023.6064 288 (0, 0, 2) (0, 0, 0, 12) n 1038.0866 1045.2327 289 (0, 0, 1) (0, 0, 0, 12) n 1073.558 1078.3221 290 (0, 0, 0) (0, 0, 1, 12) n 1103.419 1108.1831 291 (0, 0, 0) (0, 0, 0, 12) n 1172.8235 1175.2055 [292 rows x 5 columns] 0. Estimator (bic=776.43): SARIMAX(0, 1, 1)x(0, 0, 0, 12) [c,False] 1. Estimator (bic=777.08): SARIMAX(0, 1, 1)x(0, 0, 0, 12) [ct,False] 2. Estimator (bic=777.36): SARIMAX(0, 1, 1)x(0, 0, 0, 12) [n,False] 3. Estimator (bic=778.09): SARIMAX(1, 1, 0)x(0, 0, 0, 12) [n,False] 4. Estimator (bic=778.49): SARIMAX(1, 1, 0)x(0, 0, 0, 12) [c,False] 5. Estimator (bic=779.44): SARIMAX(2, 1, 0)x(0, 0, 0, 12) [c,False] 6. Estimator (bic=779.99): SARIMAX(0, 1, 0)x(0, 0, 0, 12) [n,False] 7. Estimator (bic=780.16): SARIMAX(2, 1, 0)x(0, 0, 0, 12) [ct,False] 8. Estimator (bic=780.46): SARIMAX(0, 1, 1)x(0, 0, 0, 12) [t,False] | .. code-block:: default :lineno-start: 8 # Import. import sys import warnings import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt # Import sarimax from statsmodels.tsa.statespace.sarimax import SARIMAX # import weights. from pyamr.datasets.load import make_timeseries from pyamr.core.regression.sarimax import SARIMAXWrapper # Filter warnings #warnings.simplefilter(action='ignore', category=FutureWarning) # ---------------------------- # set basic configuration # ---------------------------- # Matplotlib options mpl.rc('legend', fontsize=6) mpl.rc('xtick', labelsize=6) mpl.rc('ytick', labelsize=6) # Set pandas configuration. pd.set_option('display.max_colwidth', 14) pd.set_option('display.width', 150) pd.set_option('display.precision', 4) # ---------------------------- # create data # ---------------------------- # Create timeseries data x, y, f = make_timeseries() # Create exogenous variable exog = x # Variables. s, e = 50, 120 # ------------------------------- # create arima model # ------------------------------- # This example shows how to use auto to find the best overall model using # a particular seletion criteria. It also demonstrates how to plot the # resulting data for visualization purposes. Note that it only prints # the top best classifier according to the information criteria. # Find the best arima model (bruteforce). models, best = SARIMAXWrapper(estimator=SARIMAX) \ .auto(endog=y[:80], ic='bic', max_ar=2, max_ma=3, max_d=1, max_P=1, max_D=0, max_Q=1, list_s=[12], return_fits=True) # Sort the list (from lower to upper) models.sort(key=lambda x: x.bic, reverse=False) # Summary summary = SARIMAXWrapper().from_list_dataframe(models) # Show summary print("\nSummary:") print(summary[['sarimax-order', 'sarimax-seasonal_order', 'sarimax-trend', 'sarimax-aic', 'sarimax-bic']]) # ------------------------------- # plot results # ------------------------------- # Create figure fig, axes = plt.subplots(3,3, figsize=(10,6)) axes = axes.flatten() # Loop for the selected models for i,estimator in enumerate(models[:9]): # Show information print("%2d. Estimator (bic=%.2f): %s " % \ (i, estimator.bic, estimator._identifier())) # Get the predictions preds = estimator.get_prediction(start=s, end=e, dynamic=False) # Plot truth values. axes[i].plot(y, color='#A6CEE3', alpha=0.5, marker='o', markeredgecolor='k', markeredgewidth=0.5, markersize=5, linewidth=0.75, label='Observed') # Plot forecasted values. axes[i].plot(preds[0,:], preds[1,:], color='#FF0000', alpha=1.00, linewidth=2.0, label=estimator._identifier()) # Plot the confidence intervals. axes[i].fill_between(preds[0,:], preds[2,:], preds[3,:], color='#FF0000', alpha=0.25) # Configure axes axes[i].legend(loc=3) axes[i].grid(True, linestyle='--', linewidth=0.25) # Set superior title plt.suptitle("Non-dynamic predictions for SARIMAX") # Show plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 1 minutes 50.105 seconds) .. _sphx_glr_download__examples_forecasting_plot_sarimax_search.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_sarimax_search.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_sarimax_search.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_