.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "_examples\forecasting\plot_arima_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_arima_basic.py: ARIMA - Basic --------------------- .. GENERATED FROM PYTHON SOURCE LINES 6-148 .. image-sg:: /_examples/forecasting/images/sphx_glr_plot_arima_basic_001.png :alt: ARIMA non-dynamic, ARIMA dynamic :srcset: /_examples/forecasting/images/sphx_glr_plot_arima_basic_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Series: arima-aic 768.7153 arima-bic 775.8614 arima-hqic 771.5804 arima-llf -381.3576 arima-const_coef 285.1589 arima-const_std 139.2149 arima-const_tvalue 2.0483 arima-const_tprob 0.0405 arima-const_cil 12.3026 arima-const_ciu 558.0151 arima-ar.L1_coef 0.9891 arima-ar.L1_std 0.0196 arima-ar.L1_tvalue 50.4377 arima-ar.L1_tprob 0.0 arima-ar.L1_cil 0.9507 arima-ar.L1_ciu 1.0275 arima-sigma2_coef 771.4041 arima-sigma2_std 144.7181 arima-sigma2_tvalue 5.3304 arima-sigma2_tprob 0.0 arima-sigma2_cil 487.7618 arima-sigma2_ciu 1055.0465 arima-m_dw 1.6384 arima-m_jb_value 1249.5817 arima-m_jb_prob 0.0 arima-m_skew -3.2238 arima-m_kurtosis 21.2566 arima-m_nm_value 87.3962 arima-m_nm_prob 0.0 arima-m_ks_value 0.55 arima-m_ks_prob 0.0 arima-m_shp_value 0.7607 arima-m_shp_prob 0.0 arima-m_ad_value 2.5097 arima-m_ad_nnorm False arima-converged True arima-endog [43.200454... arima-order (1, 0, 0) arima-trend c arima-disp 0 arima-model |z| [0.025 0.975] ------------------------------------------------------------------------------ const 285.1589 139.215 2.048 0.041 12.303 558.015 ar.L1 0.9891 0.020 50.438 0.000 0.951 1.028 sigma2 771.4041 144.718 5.330 0.000 487.762 1055.046 ============================================================================== Manual ------------------------------------------------------------------------------ Omnibus: 0.000 Durbin-Watson: 1.638 Prob(Omnibus): 0.000 Jarque-Bera (JB): 1249.582 Skew: -3.224 Prob(JB): 0.000 Kurtosis_m: 21.257 Cond No: Normal (N): 87.396 Prob(N): 0.000 ============================================================================== | .. code-block:: default :lineno-start: 6 # Import. import sys import warnings import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt # Import ARIMA from statsmodels. from statsmodels.tsa.arima.model import ARIMA # import weights. from pyamr.datasets.load import make_timeseries from pyamr.core.regression.arima import ARIMAWrapper # 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 arima model. arima = ARIMAWrapper(estimator=ARIMA).fit( \ endog=y[:80], order=(1,0,0), trend='c', disp=0) # Print series print("\nSeries:") print(arima.as_series()) # Print summary. print("\nSummary:") print(arima.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 = arima.get_prediction(start=s, end=e, dynamic=False) preds_2 = arima.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=arima._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=arima._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 0.174 seconds) .. _sphx_glr_download__examples_forecasting_plot_arima_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_arima_basic.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_arima_basic.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_