Sarima

使用 SARIMA 建模季节性时间序列预测(forecast)

Zafer-Liu 8e17c73 1.0 KB Updated

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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

Zafer-Liu/Data-Analysis-Agent commit 8e17c73dcf

Frequently asked questions

npx skillmds@latest add zafer-liu/sarima