objectif

Ajuster un modèle SARIMAX simple et prévoir.

code minimal

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

idx = pd.date_range("2024-01-01", periods=50, freq="D")
y = pd.Series(np.sin(np.arange(50)/7.0) + np.random.default_rng(0).normal(scale=0.2, size=50), index=idx)
model = SARIMAX(y, order=(1,1,1), seasonal_order=(1,1,1,7)).fit(disp=False)
print(model.forecast(3).shape[0] == 3)

utilisation

pred = model.get_forecast(steps=5)
print(len(pred.predicted_mean))

variante(s) utile(s)

# model = SARIMAX(y, order=(p,d,q), seasonal_order=(P,D,Q,s))
print("ok")

notes

• Toujours vérifier les résidus.