Ennustearviot

Ennustearviot is a Finnish term referring to the evaluation of forecasts and predictions. It denotes the systematic assessment of forecast quality, including accuracy, calibration, sharpness, and usefulness for decision making. The word combines ennuste (forecast) and arviot (evaluations), and it is used in Finnish scientific and professional contexts to describe how forecasts are judged and compared.

In practice, ennustearviot cover both point forecasts and probabilistic forecasts. They involve retrospective testing on historical data, model comparisons, and the application of forecast verification methods. Common elements include choosing appropriate metrics, validating models, and communicating forecast performance to stakeholders.

Typical methods and measures include error metrics such as mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) for point forecasts. For probabilistic forecasts, scoring rules like the Brier score or proper log scores are used, along with diagnostic tools such as reliability diagrams and sharpness assessments. Calibration examines how well forecast probabilities align with observed frequencies, while sharpness measures the concentration of forecast distributions. Ensemble forecasting and cross-validation are frequently employed to improve robustness and provide uncertainty estimates.

Fields of application include meteorology, economics and finance, energy demand, public health, and other areas relying on predictive models. Ennustearviot support model selection, model improvement, and effective communication of forecast limitations to decision makers, emphasizing both accuracy and practical usefulness. Challenges include data quality, non-stationarity, regime shifts, and the risk of overfitting, which all influence how forecasts are evaluated.