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Automatic time series forecasting: the forecast package for R

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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.

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File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2007/wp6-07.pdf
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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 6/07.

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Length: 30 pages
Date of creation: Jun 2007
Date of revision:
Handle: RePEc:msh:ebswps:2007-6

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Keywords: ARIMA models; automatic forecasting; exponential smoothing; prediction intervals; state space models; time series; R.;

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  1. repec:sae:niesru:v:167:y::i:1:p:106-112 is not listed on IDEAS
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  7. Anne B. Koehler & Rob J. Hyndman & Ralph D. Snyder & J. Keith Ord, 2005. "Prediction intervals for exponential smoothing using two new classes of state space models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 17-37.
  8. Hendry, David F, 1997. "The Econometrics of Macroeconomic Forecasting," Economic Journal, Royal Economic Society, vol. 107(444), pages 1330-57, September.
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  11. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
  12. 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.
  13. Rob J Hyndman & Maxwell L. King & Ivet Pitrun & Baki Billah, 2002. "Local Linear Forecasts Using Cubic Smoothing Splines," Monash Econometrics and Business Statistics Working Papers 10/02, Monash University, Department of Econometrics and Business Statistics.
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  17. Rob J. Hyndman & Anne B. Koehler, 2005. "Another Look at Measures of Forecast Accuracy," Monash Econometrics and Business Statistics Working Papers 13/05, Monash University, Department of Econometrics and Business Statistics.
  18. Goodrich, Robert L., 2000. "The Forecast Pro methodology," International Journal of Forecasting, Elsevier, vol. 16(4), pages 533-535.
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  20. Rob J. Hyndman & Andrey V. Kostenko, 2007. "Minimum Sample Size requirements for Seasonal Forecasting Models," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 12-15, Spring.
  21. Smith, Jeremy & Yadav, Sanjay, 1994. "Forecasting costs incurred from unit differencing fractionally integrated processes," International Journal of Forecasting, Elsevier, vol. 10(4), pages 507-514, December.
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  23. Canova, Fabio & Hansen, Bruce E, 1995. "Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 237-52, July.
  24. Rob Hyndman & Muhammad Akram & Blyth Archibald, 2008. "The admissible parameter space for exponential smoothing models," Annals of the Institute of Statistical Mathematics, Springer, vol. 60(2), pages 407-426, June.
  25. Ord, J.K. & Koehler, A. & Snyder, R.D., 1995. "Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models," Monash Econometrics and Business Statistics Working Papers 4/95, Monash University, Department of Econometrics and Business Statistics.
  26. Chatfield, Chris & Yar, Mohammed, 1991. "Prediction intervals for multiplicative Holt-Winters," International Journal of Forecasting, Elsevier, vol. 7(1), pages 31-37, May.
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