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Some evidence on forecasting time-series with support vector machines

Author

Listed:
  • J V Hansen

    (Marriott School of Management, Brigham Young University)

  • J B McDonald

    (Brigham Young University)

  • R D Nelson

    (Marriott School of Management, Brigham Young University)

Abstract

The importance of predicting future values of a time-series transcends a range of disciplines. Economic and business time-series are typically characterized by trend, cycle, seasonal, and random components. Powerful methods have been developed to capture these components by specifying and estimating statistical models. These methods include exponential smoothing, autoregressive integrated moving average (ARIMA), and partially adaptive estimated ARIMA models. New research in pattern recognition through machine learning offers innovative methodologies that can improve forecasting performance. This paper presents a study of the comparative results of time-series analysis on nine problem domains, each of which exhibits differing time-series characteristics. Comparative analyses use ARIMA selection employing an intelligent agent, ARIMA estimation through partially adaptive methods, and support vector machines. The results find that support vector machines weakly dominate the other methods and achieve the best results in eight of nine different data sets.

Suggested Citation

  • J V Hansen & J B McDonald & R D Nelson, 2006. "Some evidence on forecasting time-series with support vector machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(9), pages 1053-1063, September.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:9:d:10.1057_palgrave.jors.2602073
    DOI: 10.1057/palgrave.jors.2602073
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    References listed on IDEAS

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    1. McDonald, James B. & Newey, Whitney K., 1988. "Partially Adaptive Estimation of Regression Models via the Generalized T Distribution," Econometric Theory, Cambridge University Press, vol. 4(3), pages 428-457, December.
    2. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
    3. Ord, Keith & Hibon, Michele & Makridakis, Spyros, 2000. "The M3-Competition1," International Journal of Forecasting, Elsevier, vol. 16(4), pages 433-436.
    4. J V Hansen & R D Nelson, 2003. "Forecasting and recombining time-series components by using neural networks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(3), pages 307-317, March.
    5. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, March.
    6. McDonald, James B. & Xu, Yexiao, 1994. "Some forecasting applications of partially adaptive estimators of ARIMA models," Economics Letters, Elsevier, vol. 45(2), pages 155-160, June.
    7. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
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