Individual skill
forecast-accuracy-review
Evaluate demand-forecast quality honestly - WMAPE, bias and Forecast Value Added against a naive benchmark over a rolling-origin backtest. Use when the user mentions forecast accuracy, MAPE, demand planning performance, tahmin doğruluğu, talep tahmini, or asks whether a forecasting process or tool is worth it. Differentiator - judges the process (value added vs doing nothing), not just the model.
skills-imported/forecast-accuracy-review/SKILL.md
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# Forecast Accuracy Review
A forecast is only worth what it adds over the free alternative: shipping last period's number. Every review must answer "how many points does this process add over naive?" before any model discussion.
## Required data
Per-SKU demand history at the planning bucket (usually monthly): `sku`, `period`, `qty`. If evaluating an existing forecast, also the forecast values with their creation dates (to avoid hindsight leakage). 18+ periods per SKU for a meaningful backtest; flag SKUs with less.
## Workflow
1. **Profile the demand first.** Per SKU compute mean, CV and zero-period share; classify smooth / erratic / intermittent / lumpy (defaults: CV 0.5 and 1.0 boundaries, intermittency at >25% zero periods - state them, adjust to natural breaks). Accuracy expectations differ by class; never report one blended number alone.
2. **Set the benchmarks.** Naive (last period) always; seasonal naive when 2+ full seasons exist. These are non-negotiable controls.
3. **Backtest rolling-origin.** One-step-ahead forecasts for each of the last 6+ periods, expanding window, using only data before each origin. A single train/test split is one lucky draw - do not accept it.
4. **Score with honest metrics:**