A simple randomized algorithm for consistent sequential prediction of ergodic time series

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dc.contributor	Universitat Pompeu Fabra. Departament d'Economia i Empresa
dc.contributor.author	Györfi, László
dc.contributor.author	Lugosi, Gábor
dc.contributor.author	Morvai, Gusztáv
dc.date	1998-04-01
dc.identifier.citation	https://econ-papers.upf.edu/ca/paper.php?id=282
dc.identifier.citation	IEEE Transactions on Information Theory, 45, (1999), pp. 2642-2650
dc.identifier.uri	http://hdl.handle.net/10230/922
dc.format	application/pdf
dc.language.iso	eng
dc.relation	Economics and Business Working Papers Series; 282
dc.rights	L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	prediction
dc.subject	ergodic processes
dc.subject	pattern classification
dc.subject	Statistics, Econometrics and Quantitative Methods
dc.title	A simple randomized algorithm for consistent sequential prediction of ergodic time series
dc.type	info:eu-repo/semantics/workingPaper
dc.description.abstract	We present a simple randomized procedure for the prediction of a binary sequence. The algorithm uses ideas from recent developments of the theory of the prediction of individual sequences. We show that if thesequence is a realization of a stationary and ergodic random process then the average number of mistakes converges, almost surely, to that of the optimum, given by the Bayes predictor.