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Strategies for Sequential Prediction of Stationary Time Series

In: Modeling Uncertainty

Author

Listed:
  • László Gyöfi

    (Technical University of Budapest)

  • Gábor Lugosi

    (Pompeu Fabra University)

Abstract

We present simple procedures for the prediction of a real valued sequence. The algorithms are based on a combination of several simple predictors. We show that if the sequence is a realization of a bounded stationary and ergodic random process then the average of squared errors converges, almost surely, to that of the optimum, given by the Bayes predictor. We offer an analog result for the prediction of stationary gaussian processes.

Suggested Citation

  • László Gyöfi & Gábor Lugosi, 2002. "Strategies for Sequential Prediction of Stationary Time Series," International Series in Operations Research & Management Science, in: Moshe Dror & Pierre L’Ecuyer & Ferenc Szidarovszky (ed.), Modeling Uncertainty, chapter 0, pages 225-248, Springer.
    • Handle: RePEc:spr:isochp:978-0-306-48102-4_11
    DOI: 10.1007/0-306-48102-2_11
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    Cited by:

    1. Sancetta, A., 2005. "Forecasting Distributions with Experts Advice," Cambridge Working Papers in Economics 0517, Faculty of Economics, University of Cambridge.
    2. Sancetta, Alessio, 2007. "Online forecast combinations of distributions: Worst case bounds," Journal of Econometrics, Elsevier, vol. 141(2), pages 621-651, December.

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