Forum Discussion
ML Model - SARIMAX
Hi LB_Team ,
If your data shows seasonal patterns and you want to forecast for 7 days, then SARIMAX is sensitive. Your model probably learned seasonal trends well over long periods, but struggles to generalize for short-term prediction due to overfitting to long cycles or reacting too much to end-point anomalies.Check residuals near the end of your training window.
If residuals are high, the model is already misfitting — so short-term forecasts will diverge quickly.
Tailor your model specifically for weekly forecasts-->s=7
Use smaller seasonal_order and possibly a narrower training window-- SARIMAX(..., seasonal_order=(P,D,Q,7))
I hope this helps!
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Thank You
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!
If the response has addressed your query, please accept it as a solution so other members can easily find it.
Thank You