Automatic time series forecasting: the forecast package for R

Rob Hyndman and Yeasmin Khandakar (yeasmin.khandakar@buseco.monash.edu.au)

No 6/07, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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.

Keywords: ARIMA models; automatic forecasting; exponential smoothing; prediction intervals; state space models; time series, R. (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2007-06
New Economics Papers: this item is included in nep-ets and nep-for
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Citations: View citations in EconPapers (47)

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Related works:
Journal Article: Automatic Time Series Forecasting: The forecast Package for R (2008)
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