Automatic time series forecasting: the forecast package for R

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Authors Robin John Hyndman, Yeasmin Khandakar
Journal/Conference Name Journal of Statistical Software
Paper Category
Paper Abstract Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.
Date of publication 2008
Code Programming Language R

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