forecastingestimation
Forecastingestimation refers to approaches that combine estimating the parameters of a predictive model with generating forecasts of future observations. It emphasizes the interdependence of parameter inference and predictive performance: parameter estimates determine the forecast behavior, while validation on out-of-sample data informs and refines the estimation process. The term is commonly used in statistics, econometrics, and data science to describe procedures where estimation and forecasting are carried out in an integrated or iterative manner.
Common methods include classical time series models such as ARIMA and exponential smoothing, which estimate dynamics
Evaluation focuses on forecast accuracy using metrics like mean absolute error, root mean squared error, and
Applications span economics, finance, meteorology, epidemiology, energy, and supply chain planning. Transparent reporting of model assumptions,