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Nonparametric sequential prediction of time series

Author

Listed:
  • Gérard Biau
  • Kevin Bleakley
  • László Györfi
  • György Ottucsák

Abstract

Time series prediction covers a vast field of everyday statistical applications in medical, environmental and economic domains. In this paper, we develop nonparametric prediction strategies based on the combination of a set of ‘experts’ and show the universal consistency of these strategies under a minimum of conditions. We perform an in-depth analysis of real-world data sets and show that these nonparametric strategies are more flexible, faster and generally outperform ARMA methods in terms of normalised cumulative prediction error.

Suggested Citation

  • Gérard Biau & Kevin Bleakley & László Györfi & György Ottucsák, 2010. "Nonparametric sequential prediction of time series," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(3), pages 297-317.
    • Handle: RePEc:taf:gnstxx:v:22:y:2010:i:3:p:297-317
    DOI: 10.1080/10485250802680730
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    References listed on IDEAS

    as
    1. László Györfi & Gábor Lugosi, 2000. "Strategies for sequential prediction of stationary time series," Economics Working Papers 507, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Durlauf, Steven N & Phillips, Peter C B, 1988. "Trends versus Random Walks in Time Series Analysis," Econometrica, Econometric Society, vol. 56(6), pages 1333-1354, November.
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    Cited by:

    1. Ardelean, Vlad & Pleier, Thomas, 2013. "Outliers & predicting time series: A comparative study," FAU Discussion Papers in Economics 05/2013, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    2. Bin Li & Steven C. H. Hoi, 2012. "Online Portfolio Selection: A Survey," Papers 1212.2129, arXiv.org, revised May 2013.
    3. Didi Sultana & Louani Djamal, 2014. "Asymptotic results for the regression function estimate on continuous time stationary and ergodic data," Statistics & Risk Modeling, De Gruyter, vol. 31(2), pages 1-22, June.

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