# Timeseries Forecasting

> Fit and evaluate forecasting models on seasonal time series such as demand, traffic or sensor readings.

- Skill: `jaycheng113/timeseries-forecasting` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jaycheng113/timeseries-forecasting`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jaycheng113/timeseries-forecasting/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: JayCheng113 (https://skillmd.com/u/jaycheng113)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/jaycheng113/timeseries-forecasting

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# Forecasting a seasonal series

Split by time, never at random. A shuffled split lets the model see the future,
and the resulting score is meaningless no matter how good it looks.

Decompose first. If trend and seasonality are additive, a linear model on lagged
features is usually enough; if the seasonal amplitude grows with the level, take
logs before fitting rather than reaching for a bigger model.

Evaluate with rolling-origin backtesting: fit on everything up to time `t`,
predict the next horizon, advance `t`, repeat. A single held-out tail measures one
draw from a distribution and will mislead you about variance.

Always report against a naive baseline — last value, or last season's value. A
surprising number of elaborate models fail to beat it.