Arima

使用 ARIMA 进行单变量时间序列预测(forecast)

Zafer-Liu cdccffa 1.0 KB Updated

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ARIMA 预测

确认时间列、频率、目标和预测区间,按时间排序并处理缺口。检查平稳性,选择合理参数,使用时间切分验证,输出预测值、区间和误差,并说明外部冲击限制。

Tool routing

  1. Use get_schema to identify the time column, target column, table name, and available covariates.
  2. Use query_data only to verify ordering, missing timestamps, frequency, and enough rows for modeling.
  3. Use run_analysis with analysis_name="Time_Series_ARIMA" for the actual forecast computation.
  4. Use generate_chart on the returned forecast or 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_ARIMA/analyze.py
  • Chart implementation: Function/Charts_generation/chart_generate.py

Zafer-Liu/Data-Analysis-Agent commit cdccffa7b4

Frequently asked questions

npx skillmds add zafer-liu/arima