Forecasting non-stationary time series by wavelet process modelling

Piotr Fryzlewicz, Sébastien van Bellegem and Rainer von Sachs

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Many time series in the applied sciences display a time-varying second order structure. In this article, we address the problem of how to forecast these nonstationary time series by means of non-decimated wavelets. Using the class of Locally Stationary Wavelet processes, we introduce a new predictor based on wavelets and derive the prediction equations as a generalisation of the Yule-Walker equations. We propose an automatic computational procedure for choosing the parameters of the forecasting algorithm. Finally, we apply the prediction algorithm to a meteorological time series.

JEL-codes: C1 (search for similar items in EconPapers)
Date: 2003-12
References: View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Published in Annals of the Institute of Statistical Mathematics, December, 2003, 55(4), pp. 737-764. ISSN: 0020-3157

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:25830

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