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Accuracy & evaluation

WAPE, MAPE, backtests, and intervals — and whether the number is good enough to plan on.

8 min

WAPE, explained

WAPE (weighted absolute percentage error) is total absolute error divided by total actuals. It behaves when some periods are zero, and it does not let tiny SKUs dominate the score the way MAPE does.

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10 min

Choosing a forecast accuracy metric

Pick the metric that matches the cost of being wrong. WAPE for volume, MAE when units are the pain, RMSE when big misses hurt more than small ones, bias when you always run heavy or light.

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9 min

How to backtest a forecasting model

Backtesting asks the model to forecast a past window it was not trained on, then scores the miss. Walk the window forward so you see many 'futures,' not one lucky test month.

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8 min

What is good forecast accuracy?

Good is beating a naive baseline on the same grain, horizon, and folds — by enough to change a decision. Industry round numbers are gossip until they match your SKU mix and your clock.

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8 min

Prediction intervals

A prediction interval is a range the future value is expected to fall in, not a promise. p50 is the middle path; p10–p90 is a band for planning stock, staff, and cash without pretending the future is a single line.

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8 min

Forecast value added

Forecast value added (FVA) asks whether each layer — the naive, the model, the planner’s override — actually reduced error. If the override makes WAPE worse, the meeting is a cost center, not a control tower.

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Glossary in this topic

Backtesting Walk-forward validation WAPE MAPE sMAPE MAE RMSE MASE Forecast bias Baseline forecast Prediction interval Quantile forecast

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