Nonparametric sequential prediction of time series

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dc.creatorBiau, Gérard
dc.creatorBleakley, Kevin
dc.creatorGyörfi, László
dc.creatorOttucsák, György
dc.date2008-01-02
dc.date.accessioned2026-07-07T08:52:06Z
dc.date.available2026-07-07T08:52:06Z
dc.descriptionTime series prediction covers a vast field of every-day 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 normalized cumulative prediction error.
dc.descriptionarticle + 2 figures
dc.identifierhttps://arxiv.org/abs/0801.0327
dc.identifierhttp://arxiv.org/abs/0801.0327
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/145166
dc.subjectMethodology
dc.subjectProbability
dc.subject62G99
dc.titleNonparametric sequential prediction of time series
dc.typetext

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