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How does Zansa use residuals to check a forecast is trustworthy?
Zansa treats residuals, the gaps between actual results and model predictions, as a diagnostic tool. If they look like random noise, the model has captured the main patterns. If they show structure, such as errors on holidays or growing with volume, the model is missing something and needs refining before it is relied upon.
This reflects the origin of the name, from the Japanese 残差 (zansa), meaning residual.
Typical residual checks include plotting errors over time, by weekday and month, around promotions and across product groups, looking for consistent over- or under-prediction, and checking whether the spread of errors is stable. Findings feed into model changes, such as adding a holiday flag, and into the width of prediction ranges shown to users, so forecasts are presented honestly.
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