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Automatic Time Series Forecasting: The forecast Package for R

  • Rob J. Hyndman
  • Yeasmin Khandakar

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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Article provided by American Statistical Association in its journal Journal of Statistical Software.

Volume (Year): 27 ()
Issue (Month): i03 ()
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Handle: RePEc:jss:jstsof:27:i03
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  1. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
  2. 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.
  3. Melard, G. & Pasteels, J. -M., 2000. "Automatic ARIMA modeling including interventions, using time series expert software," International Journal of Forecasting, Elsevier, vol. 16(4), pages 497-508.
  4. Kwiatkowski, D. & Phillips, P.C.B. & Schmidt, P., 1990. "Testing the Null Hypothesis of Stationarity Against the Alternative of Unit Root : How Sure are we that Economic Time Series have a Unit Root?," Papers 8905, Michigan State - Econometrics and Economic Theory.
  5. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
  6. Hyndman, R.J. & Billah, B., 2001. "Unmasking the Theta Method," Monash Econometrics and Business Statistics Working Papers 5/01, Monash University, Department of Econometrics and Business Statistics.
  7. Hyllerberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal Integration And Cointegration," Papers 0-88-2, Pennsylvania State - Department of Economics.
  8. Hyndman, R.J. & Koehler, A.B. & Ord, J.K. & Snyder, R.D., 2001. "Prediction Intervals for Exponential Smoothing State Space Models," Monash Econometrics and Business Statistics Working Papers 11/01, Monash University, Department of Econometrics and Business Statistics.
  9. Lydia Shenstone & Rob J. Hyndman, 2003. "Stochastic models underlying Croston's method for intermittent demand forecasting," Monash Econometrics and Business Statistics Working Papers 1/03, Monash University, Department of Econometrics and Business Statistics.
  10. Reilly, David, 2000. "The AUTOBOX system," International Journal of Forecasting, Elsevier, vol. 16(4), pages 531-533.
  11. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, March.
  12. Archibald, Blyth C., 1990. "Parameter space of the Holt-winters' model," International Journal of Forecasting, Elsevier, vol. 6(2), pages 199-209, July.
  13. Rob J. Hyndman & Yeasmin Khandakar, . "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, American Statistical Association, vol. 27(i03).
  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. 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.
  16. Hyndman, R.J. & Koehler, A.B. & Snyder, R.D. & Grose, S., 2000. "A State Space Framework for Automatic Forecasting Using Exponential Smoothing Methods," Monash Econometrics and Business Statistics Working Papers 9/00, Monash University, Department of Econometrics and Business Statistics.
  17. repec:sae:niesru:v:167:y::i:1:p:106-112 is not listed on IDEAS
  18. 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.
  19. Everette S. Gardner, Jr. & Ed. Mckenzie, 1985. "Forecasting Trends in Time Series," Management Science, INFORMS, vol. 31(10), pages 1237-1246, October.
  20. Chatfield, Chris & Yar, Mohammed, 1991. "Prediction intervals for multiplicative Holt-Winters," International Journal of Forecasting, Elsevier, vol. 7(1), pages 31-37, May.
  21. Hendry, David F, 1997. "The Econometrics of Macroeconomic Forecasting," Economic Journal, Royal Economic Society, vol. 107(444), pages 1330-57, September.
  22. 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.
  23. 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.
  24. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
  25. 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.
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  27. 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.
  28. 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.
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