Working Papers 2000 – Abstracts
9/2000
A State Space Framework for Automatic Forecasting Using Exponential
Smoothing Methods
Rob J. Hyndman, Anne B. Koehler, Ralph D. Snyder and Simone Grose
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.
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