gru
使用 GRU 对足量时间序列进行预测（forecast deep learning）
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- Zafer-Liu/Data-Analysis-Agent
- Last source activity
- July 17, 2026 at 13:24
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Source instructions · Read-only preview- name
- gru
- description
- 使用 GRU 对足量时间序列进行预测（forecast deep learning）
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- allowedTools
- ["get_schema","query_data","run_analysis","generate_chart"]
# 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`
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