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A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods

Rob Hyndman (), A.B. Koehler, Ralph Snyder () and S. Grose

No 9/00, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: We provide a new approach to automatic business forecasting based on an extended range of exponential smoothing methods. Each method in our taxonomy of exponential smoothing methods can be shown to be equivalent to the forecasts obtained from a state space model. This allows (1) the easy calculation of the likelihood, the AIC and other model selection criteria; (2) the computation of prediction intervals for each method; and (3) random simulation from the underlying state space model. We demonstrate the methods by applying them to the data from the M-competition on the M3-competition.

Keywords: Automatic forecasting; exponential smoothing; prediction intervals; state space models. (search for similar items in EconPapers)
JEL-codes: C50 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets
Date: 2000-08
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