A Pedant's Approach to Exponential Smoothing

Abstract

An approach to exponential smoothing that relies on a linear single source of error state space model is outlined. A maximum likelihood method for the estimation of associated smoothing parameters is developed. Commonly used restrictions on the smoothing parameters are rationalised. Issues surrounding model identification and selection are also considered. It is argued that the proposed revised version of exponential smoothing provides a better framework for forecasting than either the Box-Jenkins or the traditional multi-disturbance state space approaches.Time Series Analysis, Prediction, Exponential Smoothing, ARIMA Models, Kalman Filter, State Space Models