In This Article

Why MAPE Misleads

The Four-Metric Framework

Python Implementation

Residual Analysis and the Ljung-Box Test

Retrain vs. Recalibrate Decision Table

Why MAPE Misleads

Mean Absolute Percentage Error is the default metric for forecast evaluation in most business contexts. It is easy to explain: if your MAPE is 8%, your model is wrong by 8% on average. That simplicity is also its critical flaw.

MAPE is undefined when actuals are zero — which happens constantly in revenue series with seasonal gaps, new product launches, or promotional periods. More subtly, it penalizes over-forecasts more severely than under-forecasts by construction: a 50% under-forecast has a maximum error contribution of 100%, while an over-forecast of equal magnitude can produce an error of 200% or more.…