Forecast

Forecast is a prediction of future conditions or events, based on analysis of present data, historical patterns, and models. The term applies to any domain where future outcomes are estimated, including weather, economics, energy demand, and population trends. The word originates from Old English forecást, combining for- “before” and cást “to throw, cast,” reflecting an act of projecting what lies ahead.

In meteorology, weather forecasting uses observations from satellites and ground stations, numerical weather prediction models, and statistical methods to estimate future atmospheric states over hours to days. Economic forecasting relies on time series data, econometric models, and scenario analysis to project variables such as growth, inflation, and employment. Other applications include hydrology, epidemiology, and energy planning.

Methods of forecasting rely on data collection, model construction, and, increasingly, expert judgement. Forecasts may be point estimates, probabilistic forecasts, or interval estimates. Ensemble methods create multiple forecasts to express uncertainty, and forecasts are updated as new data arrive. Communication of uncertainty is integral, with probabilities and confidence ranges often accompanying point predictions.

Evaluation of forecast quality uses verification against observed outcomes, employing metrics such as mean absolute error, root mean squared error, and the Brier score for probabilistic forecasts. Calibration and sharpness assessments, along with hindcasting or backtesting, help gauge model performance and guide improvements.

Horizon and limitations: forecast accuracy generally declines with longer lead times. Uncertainty arises from model error, data limitations, and inherent randomness in systems. Clear articulation of uncertainty and domain-specific expectations is essential for making informed decisions based on forecasts.