forecast-accuracy-reviewlisted

# 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:** - **WMAPE** = sum(|error|) / sum(actual) - the volume-weighted headline - **Bias** = sum(error) / sum(actual) - direction; a fine WMAPE with persistent bias is quietly building excess stock or stockouts - MAPE only as a footnote, and always disclose how man