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genpark-time-series-exponential-smoothing-forecaster-skill

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by alphaparkinc
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Python
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genpark-time-series-exponential-smoothing-forecaster-skill

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

⚡ Overview & Architectural Significance

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

flowchart TD
    DataStream["Time-Series & Multidimensional Numerical Stream"] --> AnomalyDetector["Statistical Z-Score & IQR Anomaly Detector"]
    
    AnomalyDetector -->|Outlier Detected| FlagAnomaly["Flag Outlier & Alert Telemetry"]
    AnomalyDetector -->|Clean Stream| ModelingBranch{"Analytical Objective"}
    
    ModelingBranch -->|Trend Forecasting| HoltForecaster["Holt Linear Exponential Smoothing Forecaster"]
    ModelingBranch -->|Supervised Fitting| LinearRegression["Multivariate Gradient Descent Regression Engine"]
    ModelingBranch -->|Hypothesis Testing| WelchTest["Welch's Two-Sample T-Test Evaluator"]
    ModelingBranch -->|Dimensionality Reduction| PCAReducer["Power Iteration SVD/PCA Dimension Reducer"]
    
    HoltForecaster --> Synthesis["Agent Synthesis & Statistical Report"]
    LinearRegression --> Synthesis
    WelchTest --> Synthesis
    PCAReducer --> Synthesis

🚀 Quickstart & Standalone Execution

Local Python Client Usage

from client import TimeSeriesExponentialSmoothingForecaster

# Initialize engine
engine = TimeSeriesExponentialSmoothingForecaster()

# Execute self-testing benchmark suite
result = engine.run_benchmark_forecaster()
print("Execution Result:", result)

🔌 One-Click MCP Integration (Claude Desktop / Cursor)

Add to your claude_desktop_config.json or cursor.json:

{
  "mcpServers": {
    "genpark-time-series-exponential-smoothing-forecaster-skill": {
      "command": "python",
      "args": ["-u", "/path/to/genpark-time-series-exponential-smoothing-forecaster-skill/mcp_server.py"]
    }
  }
}

📦 Smithery.ai & PyPI Deployment

This skill contains pre-configured smithery.yaml and pyproject.toml manifests. Install directly via pip:

pip install git+https://github.com/alphaparkinc/genpark-time-series-exponential-smoothing-forecaster-skill.git

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