Paper #507

Título:

Strategies for sequential prediction of stationary time series

Autores:

László Györfi y Gábor Lugosi

Data:

Septiembre 2000

Resumen:

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.

Palabras clave:

Sequential prediction, ergodic process, individual sequence, gaussian process

Códigos JEL:

C13, C14

Área de investigación:

Estadística, Econometría y Métodos Cuantitativos

Publicado en:

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

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