Paper #507

Títol:

Strategies for sequential prediction of stationary time series

Autors:

László Györfi i Gábor Lugosi

Data:

Setembre 2000

Resum:

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.

Paraules clau:

Sequential prediction, ergodic process, individual sequence, gaussian process

Codis JEL:

C13, C14

Àrea de Recerca:

Estadística, Econometria i Mètodes Quantitatius

Publicat a:

In Moshe Dror, Pierre L'Ecuyer, Ferenc Szidarovszky (editors), Kluwer Academic Publishers, 2001
Amb el títol:
Modeling Uncertainty:An examination of its theory, methods, and applications (book)

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