forecastingin

Forecastingin is a term used in some scholarly discussions to describe an approach that integrates forecasting with statistical inference to quantify and communicate uncertainty about future outcomes. The concept emphasizes linking predictive models with inferential statements about underlying processes, enabling forecast users to assess both point predictions and the reliability of those predictions.

Core ideas in forecastingin include probabilistic forecasts, model calibration, and transparent uncertainty quantification. Methodologically, it draws on Bayesian and frequentist techniques, ensemble forecasting, quantile regression, and conformal prediction to produce predictive distributions or calibrated prediction intervals rather than single point estimates. This integration aims to make forecasts more interpretable for decision makers by attaching meaningful measures of uncertainty to predictions.

Evaluation in forecastingin relies on proper scoring rules, such as the continuous ranked probability score (CRPS) and Brier score, as well as interval coverage properties, calibration diagnostics, and backtesting schemes. Emphasis is placed on communicating uncertainty clearly and on validating forecast performance across different scenarios and time horizons.

Applications of forecastingin span disciplines where uncertainty matters for planning and risk management, including economics, meteorology, epidemiology, energy systems, and supply chain management. The approach supports risk-aware decision making by aligning forecast performance with the informational needs of users.

Challenges include computational complexity, data quality, model misspecification, and the difficulty of translating statistical uncertainty into actionable decisions for non-technical stakeholders. Ongoing work in forecastingin focuses on improving interpretability, scalability, and integration with decision analysis and policy design.

See also: forecasting; statistical inference; probabilistic forecasting; Bayesian forecasting; uncertainty quantification.