forecast-mcp
forecast-mcp
一个MCP服务器(模型上下文协议),将三层需求预测和补货管道作为工具暴露出来。任何MCP客户端都可以调用它:Cursor、Claude Desktop、Claude Code、Google Antigravity、Windsurf,以及其他任何支持MCP的工具。 每个序列都使用Syntetos–Boylan统计量(ADI和CV²)进行分类,并路由到一个模型:
模式 | 典型序列 | 模型 |
冷启动 | 历史数据少于14天 | 观测需求的均值 |
间歇性/块状 | 稀疏、大部分为零的需求 | TSB ( |
规律/不规则 | 连续日需求 | AutoETS ( |
阈值:ADI = 1.32,CV² = 0.49(classification.py)。
工具
工具 | 用途 |
| 加载数据集中的ID |
| 模式 + 模型层级 |
| 路由后的预测范围 |
| 留出回测(MASE) |
| 再订购点和订购数量 |
| 路由理由 |
Related MCP server: shopify-forecast-mcp
数据
服务器在启动时在内存中生成一个包含25个SKU的合成面板(规律、不规则、间歇性、块状和冷启动)。无需外部数据集或API密钥。
要使用您自己的历史数据,请传递一个包含unique_id、ds(日期)、y(单位)的CSV文件:
python -m forecast_mcp.server --data examples/sample_demand.csv
# or
export FORECAST_MCP_DATA=/path/to/demand.csv设置
python3 -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install -r requirements.txt
pip install -e ".[ui]"运行
python -m forecast_mcp.server
python -m forecast_mcp.server --transport http --port 8765
python -m forecast_mcp.uiHTTP健康检查:http://127.0.0.1:8765/health
MCP端点:http://127.0.0.1:8765/mcp
UI:http://127.0.0.1:7860
将MCP command指向此项目的.venv/bin/python。
测试
pip install pytest
pytest tests/ -v客户端配置
stdio(默认)和Streamable HTTP均受支持。示例配置位于examples/mcp/,以及.cursor/mcp.json和.agents/mcp_config.json。
{
"mcpServers": {
"forecast-mcp": {
"command": "/absolute/path/to/forecast-mcp/.venv/bin/python",
"args": ["-m", "forecast_mcp.server"]
}
}
}HTTP:
{
"mcpServers": {
"forecast-mcp": {
"url": "http://127.0.0.1:8765/mcp"
}
}
}某些客户端使用serverUrl而不是url。
扩展
冷启动:将
forecast_cold_start()中的均值回退替换为零样本基础模型(例如Chronos-Bolt)。每层额外候选:在
evaluation.py中使用MASE评分。存储:将
DataStore替换为ClickHouse、Postgres或DuckDB。具有外生特征的规律层:
mlforecast+ LightGBM。
布局
forecast-mcp/
├── src/forecast_mcp/
│ ├── server.py
│ ├── data.py
│ ├── classification.py
│ ├── forecasting.py
│ ├── evaluation.py
│ ├── replenishment.py
│ └── ui.py
├── scripts/
├── examples/
├── tests/
├── requirements.txt
└── pyproject.tomlRohan Singh · github.com/RohanSingh02
Maintenance
Resources
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