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Automatic time series forecasting: the forecast package for R

Author

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  • Rob J. Hyndman
  • Yeasmin Khandakar

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.

Suggested Citation

  • Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2007-6
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    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2007/wp6-07.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    ARIMA models; automatic forecasting; exponential smoothing; prediction intervals; state space models; time series; R.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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