forecasts

Forecasts are statements about future events or conditions produced by analyzing current data and underlying models. They are inherently probabilistic, often expressing likelihoods rather than certainties. Forecasts are produced in many domains, including weather, economics, energy, public health, and technology.

Weather forecasting uses atmospheric observations and numerical weather prediction models to predict variables such as temperature, precipitation, and wind over various lead times. Economic forecasts project growth, inflation, unemployment, and interest rates using econometric models, surveys, and expert judgment. Demographic and epidemiological forecasts estimate population trends or disease spread, while energy and climate forecasts assess future demand, supply, and climate states.

Forecasts rely on data collection and processing, statistical methods, physical or process-based models, and sometimes expert judgment. They may be point estimates, interval estimates, or probabilistic forecasts that specify a distribution over possible outcomes. Uncertainty is a central feature, influenced by model limitations, data quality, and intrinsic randomness. Ensemble forecasting—running multiple models or simulations—quantifies uncertainty and often improves reliability.

Forecast verification evaluates accuracy and calibration by comparing forecasts with observed outcomes, using metrics such as mean absolute error, root mean square error, Brier score, and reliability diagrams. Forecasts inform decision-making, risk management, and planning, but misinterpretation can lead to poor choices if uncertainty is ignored. History spans from ancient almanacs to modern numerical prediction in meteorology. Limitations include data gaps and chaotic dynamics that bound forecast skill, yet forecasts remain essential tools across many sectors.