Zero-dependency Holt linear exponential smoothing engine forecasting numerical trends with multi-step horizon and variance confidence bands.
genpark-time-series-exponential-smoothing-forecaster-skill
Production-Grade Data Science & Statistical Agent Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)
genpark-time-series-exponential-smoothing-forecaster-skill delivers zero-dependency, mathematically rigorous statistical modeling, time-series forecasting, and anomaly detection primitives engineered strictly using Python 3.9+ standard library.
🌟 Key Architectural Capabilities - Zero External Dependencies: Operates exclusively via pure Python (math, random, statistics, json). Zero pip install overhead, zero numpy/scipy compilation failures. - Enterprise Statistical Invariants: Implements formal Holt linear smoothing, modified Z-score & Tukey IQR anomaly bounds, Welch's t-test p-value estimations, multivariate linear regression via gradient descent, and Power Iteration PCA dimensionality reduction. - Native Anthropic MCP Protocol: Compliant with standard JSON-RPC 2.0 stdio MCP specifications for Claude Desktop, Cursor, and Windsurf.
🏗️ Architectural Topology & State Machine
🔌 One-Click MCP Integration (Claude Desktop / Cursor)
Add to your claudedesktopconfig.json or cursor.json:
This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:
Maintained with ❤️ by GenPark AI Engineering • Powering Next-Gen Autonomous Data Agents 🌍
From the project README.
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