A state space framework for automatic forecasting using exponential smoothing methods

 

We provide a new approach to automatic busineswwwwws 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 and the M3-competition.

Keywords: automatic forecasting, exponential smoothing, prediction intervals, state space models.

R code

Cite

@article{HKSG02,
  author  = {Rob J Hyndman and Anne B Koehler and Ralph D Snyder and Simone Grose},
  doi     = {10.1016/S0169-2070%2801%2900110-8},
  journal = {International J Forecasting},
  number  = {3},
  pages   = {439--454},
  title   = {A state space framework for automatic forecasting using exponential smoothing methods},
  volume  = {18},
  year    = 2002
}