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Automatic time series forecasting: the forecast package for R

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Author Info
Rob J. Hyndman ()
Yeasmin Khandakar ()

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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/depts/ebs/pubs/wpapers/2007/wp6-07.pdf
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Publisher 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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Related research
Keywords: ARIMA models; automatic forecasting; exponential smoothing; prediction intervals; state space models; time series; R.;

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Find related papers by JEL classification:
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June. [Downloadable!] (restricted)
  2. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454. [Downloadable!] (restricted)
    Other versions:
  3. Archibald, Blyth C., 1990. "Parameter space of the Holt-winters' model," International Journal of Forecasting, Elsevier, vol. 6(2), pages 199-209, July. [Downloadable!] (restricted)
  4. Hylleberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal, Integration And Cointegration," Papers 6-88-2, Pennsylvania State - Department of Economics.
    Other versions:
  5. 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. [Downloadable!] (restricted)
  6. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725. [Downloadable!] (restricted)
  7. 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. [Downloadable!]
  8. Hendry, David F, 1997. "The Econometrics of Macroeconomic Forecasting," Economic Journal, Royal Economic Society, vol. 107(444), pages 1330-57, September. [Downloadable!] (restricted)
  9. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530. [Downloadable!] (restricted)
  10. 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.
  11. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688. [Downloadable!] (restricted)
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  12. Goodrich, Robert L., 2000. "The Forecast Pro methodology," International Journal of Forecasting, Elsevier, vol. 16(4), pages 533-535. [Downloadable!] (restricted)
  13. 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. [Downloadable!] (restricted)
  14. 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.
  15. Chatfield, Chris & Yar, Mohammed, 1991. "Prediction intervals for multiplicative Holt-Winters," International Journal of Forecasting, Elsevier, vol. 7(1), pages 31-37, May. [Downloadable!] (restricted)
  16. Reilly, David, 2000. "The AUTOBOX system," International Journal of Forecasting, Elsevier, vol. 16(4), pages 531-533. [Downloadable!] (restricted)
  17. 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. [Downloadable!]
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Jae H. Kim & Haiyang Song & Kevin Wong & George Athanasopoulos & Shen Liu, 2008. "Beyond point forecasting: evaluation of alternative prediction intervals for tourist arrivals," Monash Econometrics and Business Statistics Working Papers 11/08, Monash University, Department of Econometrics and Business Statistics, revised Oct 2009. [Downloadable!]
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