Prognoosikontroll

Prognoosikontroll, commonly translated as forecast control, is a systematic discipline that aims to ensure the quality, reliability and transparency of forecasts produced within organizations or public institutions. It encompasses planning, development, validation, deployment, and ongoing monitoring of forecasting activities, across domains such as meteorology, energy, finance, and public health.

Core activities include data quality management, model validation and backtesting, performance monitoring, calibration of probabilistic forecasts, and explicit treatment of forecast uncertainty. A typical workflow starts with data collection and preprocessing, followed by model construction or selection, forecast generation, backtesting against observed outcomes, and reporting of results. When performance degrades or new data become available, models are recalibrated or replaced, with changes tracked under version control.

Common performance metrics cover error measures like RMSE and MAE, bias assessment, and reliability and sharpness for probabilistic forecasts, as well as domain-specific metrics. Visualization tools such as calibration plots and backtesting dashboards support interpretation. Documentation and audit trails maintain transparency and reproducibility.

Governance structures define roles (forecasters, data scientists, risk managers), procedures for validation and approval, and compliance with data governance standards. Effective prognoosikontroll reduces decision risk, improves accountability, and facilitates communication of forecast uncertainty to stakeholders. Challenges include non-stationarity, data gaps, model overfitting, and effectively conveying uncertainty to non-technical audiences.

While the term is used in Estonian contexts, prognoosikontroll reflects a broader field of forecast verification and validation that spans meteorology, economics, and public policy.