ennustatavust

Ennustatavust (predictability) is the degree to which future states or outcomes of a system can be forecast given available information, data, and models. It is a central concern in fields such as meteorology, economics, engineering, and epidemiology, where reliable forecasts support decision making and risk assessment. Predictability depends on the underlying system dynamics, the quality and quantity of data, and the suitability of the modeling approach.

Predictability is shaped by both the nature of the system and our knowledge about it. In deterministic but chaotic systems, short-term forecasts may be feasible, while long-term predictions deteriorate due to sensitivity to initial conditions. In stochastic systems, intrinsic randomness imposes a fundamental limit on accuracy. Uncertainty is often divided into aleatoric (intrinsic variability) and epistemic (gaps in knowledge or model limitations). Reducing epistemic uncertainty is a primary focus of model development, data collection, and data assimilation.

Methods to study and improve ennustatavust include ensemble forecasting, Bayesian inference, and probabilistic forecasting that quantify uncertainty rather than provide a single point estimate. Evaluation uses metrics such as mean absolute error, root mean square error, Brier score, and calibration or sharpness analyses, often across different forecast horizons. Applications range from climate and weather to finance, public health, and engineering, where understanding predictability supports risk management and planning.