SARIMAX in Python

Seasonal orders and exogenous regressors, in one model.

import numpy as np
import pandas as pd
from statsmodels.tsa.statespace.sarimax import SARIMAX

rng = np.random.default_rng(15)
n = 160
season = 6 * np.sin(2 * np.pi * np.arange(n) / 12)
practice = rng.uniform(0, 2, size=n)
series = pd.Series(90 + season + 3 * practice + rng.normal(0, 1, n))

model = SARIMAX(series, exog=practice, order=(1, 0, 0),
                seasonal_order=(1, 0, 0, 12)).fit(disp=False)
print(model.params.round(3).to_dict())

future_exog = np.full((4, 1), 1.0)
forecast = model.get_forecast(steps=4, exog=future_exog)
print(forecast.predicted_mean.round(2).tolist())

How it works

seasonal_order is (P, D, Q, s) with s the period.
exog adds outside predictors to the state space.
The forecast needs future exog values too.

Keywords and builtins used here

as
print

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