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Strategies for sequential prediction of stationary time series

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.

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File URL: http://www.econ.upf.edu/docs/papers/downloads/507.pdf
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Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 507.

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Date of creation: Sep 2000
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Handle: RePEc:upf:upfgen:507
Contact details of provider: Web page: http://www.econ.upf.edu/

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