Gru

使用 GRU 对足量时间序列进行预测(forecast deep learning)

Zafer-Liu b514c44 1.0 KB Updated

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GRU 预测

仅在样本量和序列长度足够时使用。确认窗口、特征和预测期,严格按时间划分训练验证,避免泄漏,报告基线对比、误差和不确定性;小数据优先建议传统时序模型。

Tool routing

  1. Use get_schema to identify the time column, target column, feature columns, and source table.
  2. Use query_data only to verify sorted sequence length, missingness, and whether the sample size is sufficient.
  3. Use run_analysis with analysis_name="Time_Series_GRU" for the actual model computation.
  4. Use generate_chart on forecast or validation 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_GRU/analyze.py
  • Chart implementation: Function/Charts_generation/chart_generate.py

Zafer-Liu/Data-Analysis-Agent commit b514c44d88

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