ForecastPerformance

ForecastPerformance is a term used to describe how well forecast models and forecasting processes perform in practice. It encompasses the accuracy, reliability, and usefulness of forecasts across different time horizons, variables, and domains such as meteorology, economics, energy, and supply chain.

Measures of accuracy include point-forecast error metrics such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). For probabilistic forecasts, proper scoring rules such as the continuous ranked probability score (CRPS) or the Brier score are used. Calibration assesses whether the forecast probabilities align with observed frequencies, while sharpness assesses the concentration of the predictive distribution. Interval forecasts are evaluated by coverage probabilities and interval scores.

Evaluation is typically conducted through backtesting or rolling-origin cross-validation on historical data, with out-of-sample validation to reflect performance in unseen conditions. The choice of horizon, loss function, and reference baseline affects reported ForecastPerformance. Comparisons often use statistical tests, resampling methods, or alternative scoring rules to determine whether differences are significant.

Context matters: performance depends on data quality, regime changes, model complexity, and the inherent predictability of the target variable. ForecastPerformance can improve through model ensembling, calibration techniques, feature engineering, and updating schemes. Overfitting, data leakage, and look-ahead bias must be avoided to obtain reliable assessments.

In practice, ForecastPerformance informs model selection, operational deployment, and ongoing monitoring, enabling organizations to quantify forecast value, detect degradation over time, and allocate resources accordingly.