This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
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.
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.
Related MCP server: genpark-time-series-exponential-smoothing-forecaster-skill
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 --> Synthesisfrom client import TimeSeriesExponentialSmoothingForecaster
# Initialize engine
engine = TimeSeriesExponentialSmoothingForecaster()
# Execute self-testing benchmark suite
result = engine.run_benchmark_forecaster()
print("Execution Result:", result)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"]
}
}
}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.gitRecent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
PredictOracle - 12 forecasting tools: time-series, scenario analysis, risk projections.
Forecast product demand using historical sales and market signals.
13 neural engines for time-series anomaly detection, classification, and root cause analysis.
Probabilistic time-series forecasts from zero-shot foundation models: routed, single or ensembled.
