Forecasting non-stationary time series by wavelet process modelling

PZ Fryzlewicz, S Van Bellegem, R von Sachs

Research output: Contribution to journalArticle (Academic Journal)peer-review

65 Citations (Scopus)


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 non-stationary 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.
Translated title of the contributionForecasting non-stationary time series by wavelet process modelling
Original languageEnglish
Pages (from-to)737 - 764
Number of pages28
JournalAnnals of the Institute of Statistical Mathematics
Volume55 (4)
Publication statusPublished - Dec 2003

Bibliographical note

Publisher: Springer
Other identifier: IDS number 764YT


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