Automatic forecasts of large numbers of univariate time series are often needed in business. It is common to have over one thousand product lines that need forecasting at least monthly. In these circumstances, an automatic forecasting algorithm is an essential tool. Automatic forecasting algorithms must determine an appropriate time series model, estimate the parameters and compute the forecasts. The most popular automatic forecasting algorithms are based on either exponential smoothing or ARIMA models. Exponential smoothing Although exponential smoothing methods have been around since the 1950s, a modelling framework incorporating procedures for model selection was not developed until relatively recently with the work of Ord et al. (1997) and Hyndman et al. (2002). In these (and other) papers, a class of state space models which underly all of the exponential smoothing methods has been developed. Exponential smoothing methods were originally classified by Pegels ’ (1969) taxonomy. This was later extended by Gardner (1985), modified by Hyndman et al. (2002), and extended again by Taylor (2003), giving a total of fifteen methods seen in the following table
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