Individual skill
forecasting
Use when projecting history forward — sales, demand, units, revenue, signups, traffic — into a defensible number with an error band: method by data shape, rolling-origin backtest, MASE vs the naive baseline. NOT an assumption-driven P&L or runway model (that is `financial-model`), NOT sizing reorder points or safety stock (that is `inventory`).
skills/forecasting/SKILL.md
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- forecasting Declared skill tag
- demand-planning Declared skill tag
- time-series Declared skill tag
- sales-forecast Declared skill tag
- statsforecast Declared skill tag
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SKILL.mdcore · 11,081 bytesSKILL.mdcore · 11,081 bytesSKILL.mdcore · 11,081 bytesevals/README.mddoc · 874 bytesevals/cases.yamlasset · 2,854 bytesreferences/accuracy-and-backtesting.mddoc · 3,963 bytesreferences/methods-cheatsheet.mddoc · 3,406 bytesscripts/verify.shruns · 3,378 bytes
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# Forecasting
A forecast that cannot beat "repeat last period" is noise. Baseline first, fancy second. The naive forecast is free, instant, and the bar every model must clear — if your AutoARIMA loses to last-quarter-repeated, ship the repeat and say so.
You are not done when a model produces a number. You are done when you can defend the number: which method, why that method for this data, how it scored against the naive baseline in a backtest, and the interval around the point. A point estimate with no error band is a guess wearing a lab coat.
## The deliverable contract
Every forecast you ship is a reproducible artifact, not a number pasted in chat:
1. A **script** that reads the history and regenerates the forecast (no manual steps).
2. A **CSV/Parquet** with columns `ds, forecast, lo, hi` — timestamp, point, interval bounds.
3. A **one-paragraph accuracy readout**: WAPE + bias from a rolling-origin backtest, and MASE vs the naive baseline (MASE < 1.0 = you beat naive; ≥ 1.0 = ship the naive forecast instead).
