forecást

Forecást is the act or result of predicting future events or conditions based on data, models, and judgment. It is used across many fields to inform planning and risk assessment. The term is widely used in meteorology for weather predictions, but forecást also covers economic forecasts, demand planning, energy load forecasts, and epidemiological projections. The spelling with an accent is nonstandard in many languages and may appear in stylized or language-specific contexts.

Forecasting relies on data collection, preprocessing, and model selection. Methods range from qualitative approaches, such as expert judgment and scenario analysis, to quantitative techniques, including time series analysis (ARIMA, exponential smoothing), regression models, structural models, and machine learning. Ensemble methods combine multiple forecasts to better capture uncertainty, and scenario planning explores alternative futures.

Evaluation focuses on accuracy and reliability. Common metrics include mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and probabilistic scores such as the Brier score. Forecasts are accompanied by uncertainty estimates, such as prediction intervals or probability distributions, to help users interpret risk.

Applications vary: weather prediction informs safety and planning; economic and financial forecasts guide policy, budgeting, and investment; demand forecasting supports manufacturing and retail; energy forecasts affect grid management; public health forecasts inform responses.

Forecasting has a long history, from early meteorological observations to modern data-driven models. Its effectiveness depends on data quality, model validity, and clear communication of uncertainty. Forecasts are continuously revised as new information becomes available.