Bayesian Forecasting

Summary This paper describes a Bayesian approach to forecasting. The principles of Bayesian forecasting are discussed and the formal inclusion of “the forecaster” in the forecasting system is emphasized as a major feature. The basic model, the dynamic linear model, is defined together with the Kalman filter recurrence relations and a number of model formulations are given. Multi-process models introduce uncertainty as to the underlying model itself, and this approach is described in a more general fashion than in our 1971 paper. Applications to four series are described in a sister paper. Although the results are far from exhaustive, the authors are convinced of the great benefits which the Bayesian approach offers to forecasters.