# Sarima

> 使用 SARIMA 建模季节性时间序列预测（forecast）

- Skill: `zafer-liu/sarima` (Agent Skill)
- Install (CLI): `npx skillmds@latest add zafer-liu/sarima`
- Raw SKILL.md: https://api.skillmd.com/api/skills/zafer-liu/sarima/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: Zafer-Liu (https://skillmd.com/u/zafer-liu)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/zafer-liu/sarima

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# SARIMA 预测

确认时间频率和季节周期，检查数据长度能否覆盖足够周期。执行季节模型与时间验证，报告参数、误差、预测区间和季节模式；数据不足时不要强行拟合。

## Tool routing

1. Use `get_schema` to identify the time column, target column, seasonal frequency, and source table.
2. Use `query_data` only to verify sorted frequency, missing periods, and enough seasonal cycles.
3. Use `run_analysis` with `analysis_name="Time_Series_SARIMA"` for the actual forecast computation.
4. Use `generate_chart` on forecast or seasonal diagnostic result tables after `run_analysis` succeeds.

## Implementation reference

- Tool entry: `agent/tools/business/data.py::_tool_run_analysis`
- Analysis registry: `Function/Analyze/registry.py`
- Analysis implementation: `Function/Analyze/Time_Series_SARIMA/analyze.py`
- Chart implementation: `Function/Charts_generation/chart_generate.py`