Faim Time Series Forecasting MCP Server

@S-FM

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基本信息

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生产力

传输方式

stdio

作者

S-FM

提交者

Andrei Chernov

关于 Faim Time Series Forecasting MCP Server

An MCP server for zero-shot time-series forecasting powered by foundation models such as Chronos 2 and TiRex, with support for multivariate and probabilistic forecasts.

#time-series #forecasting

Faim Time Series Forecasting MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "faim": {
      "command": "npx",
      "args": [
        "-y",
        "@faim-group/mcp"
      ],
      "env": {
        "FAIM_API_KEY": "your-api-key-here"
      }
    }
  }
}

Faim Time Series Forecasting MCP Server 工具

List all available forecasting models and their capabilities. Returns information about Chronos2, TiRex, and other available models, including supported output types and features.

Perform time series forecasting using FAIM platform. Supports both point forecasting (single value) and probabilistic forecasting (confidence intervals). Can handle univariate and multivariate time series data. Currently supported models: Chronos2 (default, recommended for multivariate) and TiRex (fast, univariate only).

概览

What is Faim Time Series Forecasting?

The Faim Time Series Forecasting server is a Model Context Protocol (MCP) server that integrates the FAIM time series forecasting SDK with any MCP-compatible AI assistant. It enables AI-powered zero-shot forecasting using two foundation models: Chronos2 and TiRex.

How to use Faim Time Series Forecasting?

Install Node.js 20+, npm 10+, and obtain a FAIM API key from faim.it.com. Configure your MCP client to run the server via npx -y @faim-group/mcp or the global faim-mcp command, setting the FAIM_API_KEY environment variable. The server provides two tools: list_models to retrieve available models and forecast to perform time series forecasting with point, quantile, or sample outputs.

Key features of Faim Time Series Forecasting

Exposes two foundation time-series models: Chronos2 and TiRex.
Offers two MCP tools: list_models and forecast.
Accepts 1D arrays for single series or 3D arrays for batch inputs.
Supports point, quantile, and sample forecast outputs.
Allows custom quantile levels for risk assessment.
Works with any MCP-compatible AI assistant.

Use cases of Faim Time Series Forecasting

Perform zero-shot time series forecasting for any univariate series.
Run batch forecasts on multiple sequences using 3D input arrays.
Generate confidence intervals via quantile predictions.
Obtain distribution samples for probabilistic analysis.
Integrate forecasting into an existing MCP-connected AI workflow.

常见问题

What models are available?

The server exposes Chronos2 and TiRex for zero-shot forecasting.

What input formats are supported?

Input can be a 1D array (single time series) or a 3D array in batch/sequence/feature format.

How do I get a FAIM API key?

Register at [https://faim.it.com/](https://faim.it.com/) to obtain your API key, which must be set as the `FAIM_API_KEY` environment variable.

What are the system requirements?

Node.js 20+, npm 10+, and a valid FAIM API key are required.

Can I use this server with any LLM?

Yes, the server implements the standard MCP protocol and works with any application that implements an MCP client.

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