Skip to main content
Glama

timeseries_guide

Guide for time series analysis using ARIMA, VAR, VECM, GARCH, and state space models with stationarity testing options.

Instructions

시계열 분석 가이드 (ARIMA, VAR, 공적분)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
analysisYes분석 유형
stationarityNo정상성 검정 포함

Implementation Reference

  • The main handler function for the 'timeseries_guide' tool. It takes input arguments and returns a structured workflow guide for timeseries analysis (e.g., ARIMA, VAR) including steps like stationarity tests and diagnostics.
    function handleTimeseriesGuide(args: Record<string, unknown>) { return { analysis: args.analysis, workflow: [ "1. Stationarity test (ADF, KPSS)", "2. Determine order (ACF, PACF, Information criteria)", "3. Estimate model", "4. Diagnostics (Ljung-Box, residual ACF)", "5. Forecasting / Impulse response" ] }; }
  • Registration of the 'timeseries_guide' tool in the exported tools array, including name, description, and input schema definition.
    { name: "timeseries_guide", description: "시계열 분석 가이드 (ARIMA, VAR, 공적분)", inputSchema: { type: "object", properties: { analysis: { type: "string", enum: ["arima", "var", "vecm", "garch", "state_space"], description: "분석 유형" }, stationarity: { type: "boolean", description: "정상성 검정 포함" }, }, required: ["analysis"], }, },
  • Tool handler dispatch in the main handleToolCall switch statement, mapping the tool name to its handler function.
    case "timeseries_guide": return handleTimeseriesGuide(args);
  • Input schema for the 'timeseries_guide' tool, defining parameters like 'analysis' (required, enum of timeseries methods) and optional 'stationarity' boolean.
    inputSchema: { type: "object", properties: { analysis: { type: "string", enum: ["arima", "var", "vecm", "garch", "state_space"], description: "분석 유형" }, stationarity: { type: "boolean", description: "정상성 검정 포함" }, }, required: ["analysis"], },

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/seanshin0214/quantmaster-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server