A Proposal for a Unified Forecast Accuracy Index (UFAI): Toward Multidimensional and Context-Aware Forecast Evaluation

Forecast accuracy evaluation is a cornerstone in fields as diverse as finance, public health, energy, and meteorology. However, traditional reliance on single-error metrics—such as MAE, RMSE, or MAPE—offers only a fragmented view of a model’s performance, often obscuring critical dimensions like systematic bias, volatility, directional behavior, or shape fidelity. To overcome these limitations, this study proposes the Unified Forecast Accuracy Index (UFAI), a multidimensional and composite metric that consolidates several facets of forecasting quality into a single, interpretable score. UFAI integrates four normalized sub-indices—bias, variance, directional accuracy, and shape preservation—each capturing a distinct performance characteristic. The framework accommodates multiple weighting schemes: equal weighting for simplicity, expert-informed weighting to reflect domain-specific priorities, and data-driven weighting based on statistical principles such as Principal Component Analysis and entropy measures. This flexibility enables users to adapt the index to diverse forecasting objectives and application contexts. The article details the mathematical formulation of each sub-index, discusses the theoretical soundness and practical implications of different weighting strategies, and demonstrates the utility of UFAI through comparative model evaluations. Emphasis is placed on the index’s normalization, interpretability, robustness to outliers, and extensibility to future use cases such as multi-horizon and probabilistic forecasts. By offering a more integrated and context-aware assessment tool, the UFAI marks a significant advancement in forecast evaluation methodology, supporting more reliable model selection and ultimately enhancing decision-making in data-driven environments.