时间序列预测 MCP (Model Context Protocol) - 提供 AR、MA、ARIMA、GARCH、指数平滑五种时间序列预测能力
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# Time Series Forecast MCP 时间序列预测 MCP (Model Context Protocol)，提供五种时间序列预测模型： - **AR**: 自回归模型 - **MA**: 移动平均模型 - **ARIMA**: 自回归积分移动平均模型（支持自动选参） - **GARCH**: 广义自回归条件异方差模型（波动率预测） - **EXPONENTIAL_SMOOTHING**: 指数平滑模型（支持季节性） ## 安装 ```bash pip install time-series-forecast-mcp ``` ## 使用 ### 作为 MCP 服务启动 ```bash python -m time_series_forecast_mcp ``` 或使用 fastmcp CLI： ```bash fastmcp run server.py:mcp ``` ### 工具列表 #### list_forecast_models 列出支持的时间序列预测模型及适用场景说明。 #### forecast_time_series 对历史时间序列进行预测。 **参数**: - `model_type`: AR / MA / ARIMA / GARCH / EXPONENTIAL_SMOOTHING - `series`: 历史观测值，按时间升序排列 - `horizon`: 向前预测步数（默认 12） - `p`: AR 阶数或 ARIMA/GARCH 的 p（可选） - `d`: ARIMA 差分阶数（可选） - `q`: MA 阶数或 ARIMA/GARCH 的 q（可选） - `seasonal_period`: 季节周期，仅 EXPONENTIAL_SMOOTHING 使用（可选） - `confidence_level`: 置信水平，默认 0.95 **返回**: - `forecast`: 点预测 - `lower_bound / upper_bound`: 置信区间 - `model_info`: 模型参数与 AIC/BIC 等信息 - `diagnostics`: 样本量等诊断信息 ## 依赖 - fastmcp >= 2.0.0 - numpy >= 1.24.0 - pandas >= 2.0.0 - statsmodels >= 0.14.0 - arch >= 7.0.0 - scipy >= 1.10.0 ## 许可证 MIT License
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