Forecast Accuracy Auditor
Computes forecast accuracy metrics like MAE, RMSE, and MAPE by comparing predictions to actual values. Data analysts and ML engineers use it to evaluate model performance in production. Applies to sales forecasting, inventory planning, and financial projections.
Overview
The Forecast Accuracy Auditor MCP server provides programmatic evaluation of forecasting models. It processes pairs of predicted and actual values to calculate standard error metrics, enabling objective assessment of prediction quality without manual computation.
Key Capabilities
- Calculates core metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).
- Compares multiple forecast versions against ground truth data.
- Outputs statistical summaries and error distributions for model diagnostics.
These functions support batch processing of time-series data via MCP protocol calls.
Use Cases
- Supply chain management: Audit monthly demand forecasts by inputting historical predictions and sales data to identify systematic biases in inventory models.
- Financial forecasting: Evaluate stock price or revenue predictions quarterly using RMSE to validate trading algorithms.
- Energy sector: Assess weather-based load forecasts with MAPE to refine grid optimization models.
- E-commerce: Review promotional sales forecasts against actual transactions to adjust future pricing strategies.
Who This Is For
Data analysts validating business forecasts, machine learning engineers tuning time-series models, and quantitative teams in finance or operations requiring reproducible accuracy checks. Integrates into CI/CD pipelines for automated model monitoring.