ML Model - SARIMAX

Hi LB_Team ,

SARIMAX captures seasonal components, trends, and exogenous variables. If your model includes seasonal components (e.g., weekly or yearly), then:

Long-term forecasts (like 12 months) are more influenced by seasonal patterns it has learned.

Short-term forecasts (like 7 days) might be more sensitive to noise, recent variability, or model overfitting to short-term anomalies.

Improper seasonal or non-seasonal orders can have unexpected short-term behavior. For example:

1.Over-differencing can cause forecasts to trend downward.

2.Misidentified seasonal periods  can distort short-term forecasts

Try using ARIMA model:
Refer-
https://skforecast.org/0.10.0/user_guides/forecasting-sarimax-arima

Hope this helps!
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Thank You