3 Statsmodels Tricks for Time Series Analysis & Forecasting

pip install statsmodels

Trick 1: Asking for the Interval, Not Just the Number

res.forecast(12) gives you twelve numbers. res.get_forecast(12) gives you a PredictionResults object instead, which happens to also carry the uncertainty the model already estimated. There's predicted_mean for the points, conf_int for the bounds, and summary_frame() for both at once. The intervals aren't even extra work; they are a result of the same computation, and the shorter method simply throws them away:

import statsmodels.api as sm from statsmodels.tsa.arima.model import ARIMA

co2 = sm.datasets.co2.load_pandas().data["co2"] co2 = co2.resample("MS").mean().ffill() train, recent = co2[:-12], co2[-12:]

res = ARIMA(train, order=(1, 1, 1), seasonal_order=(1, 1, 1, 12)).fit()

print(res.get_forecast(12).summary_frame().head())

Output:

co2 mean mean_se mean_ci_lower mean_ci_upper 2001-01-01 370.523929 0.322722 369.891406 371.156452 2001-02-01 371.253673 0.388214 370.492787 372.014559 2001-03-01 372.200726 0.429518 371.358887 373.042566 2001-04-01 373.468351 0.463501 372.559905 374.376797 2001-05-01 373.856957 0.494157 372.888427 374.825487

Publishing a forecast without its interval is a choice. And if what you want is fitted values over the history rather than the path ahead, get_prediction is this same idea applied to a range that can include in-sample periods.

Twelve months of observations arrive. The reflex is to concatenate them onto the training data and call .fit() again, which re-estimates every parameter from scratch. append does something cheaper: it recreates the results object over the combined data and, with refit=False, it keeps the parameters you already estimated:

updated = res.append(recent, refit=False)

print(updated.get_forecast(6).summary_frame().head())

Output:

co2 mean mean_se mean_ci_lower mean_ci_upper 2002-01-01 371.969954 0.322722 371.337432 372.602477 2002-02-01 372.750021 0.388214 371.989135 373.510907 2002-03-01 373.654908 0.429518 372.813068 374.496748 2002-04-01 374.834276 0.463501 373.925830 375.742722 2002-05-01 375.328719 0.494157 374.360189 376.297248

The default is refit=False, which reuses the estimates you already have. Pass refit=True when enough new data has accumulated that you want them computed again.