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A wavelet-based approach for detecting changes in second order structure within nonstationary time series

Research output: Contribution to journalJournal article

Published

Journal publication date2013
JournalElectronic Journal of Statistics
Volume7
Number of pages17
Pages1167-1183
Original languageEnglish

Abstract

This article proposes a test to detect changes in general autocovariance structure in nonstationary time series. Our approach is founded on the locally stationary wavelet (LSW) process model for time series which has previously been used for classification and segmentation of time series. Using this framework we form a likelihood-based hypothesis test and demonstrate its performance against existing methods on various simulated examples as well as applying it to a problem arising from ocean engineering.