.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "_examples\forecasting\plot_sarimax_basic.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_basic.py: SARIMAX - Basic ----------------------- See `SARIMAX `_ .. GENERATED FROM PYTHON SOURCE LINES 8-154 .. image-sg:: /_examples/forecasting/images/sphx_glr_plot_sarimax_basic_001.png :alt: ARIMA non-dynamic, ARIMA dynamic :srcset: /_examples/forecasting/images/sphx_glr_plot_sarimax_basic_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Series: sarimax-aic 764.1138 sarimax-bic 778.3305 sarimax-hqic 769.8095 sarimax-llf -376.0569 sarimax-intercept_coef 23.9663 ... sarimax-seasonal_order (1, 0, 1, 12) sarimax-order (0, 1, 1) sarimax-disp 0 sarimax-model |z| [0.025 0.975] ------------------------------------------------------------------------------ intercept 23.9663 9.263 2.587 0.010 5.811 42.121 drift -0.3219 0.137 -2.345 0.019 -0.591 -0.053 ma.L1 -0.4928 0.089 -5.521 0.000 -0.668 -0.318 ar.S.L12 -0.7897 0.520 -1.518 0.129 -1.809 0.230 ma.S.L12 0.6418 0.639 1.005 0.315 -0.610 1.894 sigma2 784.2609 143.939 5.449 0.000 502.145 1066.377 ============================================================================== Ljung-Box(L1)(Q): 0.03 Jarque-Bera (JB): 0.69 Prob(Q): 0.87 Prob(JB): 0.71 Heteroskedasticity(H): 0.84 Skew: -0.23 Prob(H)(two-sided): 0.67 Kurtosis: 3.08 ============================================================================== Manual ------------------------------------------------------------------------------ Omnibus: 0.000 Durbin-Watson: 2.000 Normal (N): 1.127 Prob(N): 0.569 ============================================================================== Note that JB, P(JB), skew and kurtosis have different values. Note that Prob(Q) tests no correlation of residuals. | .. 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 # ---------------------------- # fit the model # ---------------------------- # Create specific sarimax model. sarimax = SARIMAXWrapper(estimator=SARIMAX)\ .fit(endog=y[:80], exog=None, trend='ct', seasonal_order=(1,0,1,12), order=(0,1,1), disp=0) # Print series print("\nSeries:") print(sarimax.as_series()) # Print summary. print("\nSummary:") print(sarimax.as_summary()) # ----------------- # Save & Load # ----------------- # File location #fname = '../../examples/saved/arima-sample.pickle' # Save #arima.save(fname=fname) # Load #arima = ARIMAWrapper().load(fname=fname) # ----------------- # Predict and plot # ----------------- # This example shows how to make predictions using the wrapper which has # been previously fitted. It also demonstrateds how to plot the resulting # data for visualization purposes. It shows two different types of # predictions: # - dynamic predictions in which the prediction is done based on the # previously predicted values. Note that for the case of ARIMA(0,1,1) # it returns a line. # - not dynamic in which the prediction is done based on the real # values of the time series, no matter what the prediction was for # those values. # Variables. s, e = 50, 120 # Compute predictions preds_1 = sarimax.get_prediction(start=s, end=e, dynamic=False) preds_2 = sarimax.get_prediction(start=s, end=e, dynamic=True) # Create figure fig, axes = plt.subplots(1, 2, figsize=(8,3)) # ---------------- # Plot non-dynamic # ---------------- # Plot truth values. axes[0].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[0].plot(preds_1[0,:], preds_1[1,:], color='#FF0000', alpha=1.00, linewidth=2.0, label=sarimax._identifier()) # Plot the confidence intervals. axes[0].fill_between(preds_1[0,:], preds_1[2,:], preds_1[3,:], color='#FF0000', alpha=0.25) # ------------ # Plot dynamic # ------------ # Plot truth values. axes[1].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[1].plot(preds_2[0,:], preds_2[1,:], color='#FF0000', alpha=1.00, linewidth=2.0, label=sarimax._identifier()) # Plot the confidence intervals. axes[1].fill_between(preds_2[0,:], preds_2[2,:], preds_2[3,:], color='#FF0000', alpha=0.25) # Configure axes axes[0].set_title("ARIMA non-dynamic") axes[1].set_title("ARIMA dynamic") # Format axes axes[0].grid(True, linestyle='--', linewidth=0.25) axes[1].grid(True, linestyle='--', linewidth=0.25) # Legend axes[0].legend() axes[1].legend() # Show plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.501 seconds) .. _sphx_glr_download__examples_forecasting_plot_sarimax_basic.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_basic.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_sarimax_basic.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_