FRWMetrik

FRWMetrik is a metric framework used to evaluate the quality of forecasts and the associated risk across scenarios. It is designed to quantify not only accuracy but also the potential impact of forecast errors, enabling more informed model comparison and decision making in domains such as finance, logistics, and energy.

The framework combines forecast error, reliability (calibration), and impact weighting. Each forecast error is assigned a risk weight that reflects the consequence of being wrong in that case, often scaled by scenario probability or impact. The FRWMetrik score is typically expressed on a normalized scale, such as 0 to 100, to facilitate comparison across models and settings.

Calculation approach: for a finite set of scenarios i, with error e_i, risk weight r_i, and probability p_i, FRWMetrik is commonly computed as a weighted sum: FRWMetrik = sum_i p_i * e_i * r_i, followed by normalization. The base error can be MAE, RMSE, or MAPE, and calibration factors may be applied to reflect reliability.

Applications: used in finance for risk-adjusted forecasting, supply chain planning, energy demand forecasting, and weather or climate risk assessment, among others. It provides a single score that captures both deviation magnitude and potential consequence.

Variants and interpretation: FRWMetrik can be adapted to emphasize different aspects by adjusting weights, horizons, or error metrics. Related measures include risk-adjusted error metrics, such as risk-adjusted MAE, and calibration-focused scores that separately report reliability.

Limitations and considerations: the choice of weights and risk definitions is subjective and context-dependent. Data quality, missing values, and outliers can greatly affect the FRWMetrik score, so transparent documentation of assumptions is essential.