Electronic Journal of Statistics

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

R. Killick, I. A. Eckley, and P. Jonathan

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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.

Article information

Electron. J. Statist., Volume 7 (2013), 1167-1183.

First available in Project Euclid: 23 April 2013

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segmentation wavelets local stationarity significant wave height


Killick, R.; Eckley, I. A.; Jonathan, P. A wavelet-based approach for detecting changes in second order structure within nonstationary time series. Electron. J. Statist. 7 (2013), 1167--1183. doi:10.1214/13-EJS799. https://projecteuclid.org/euclid.ejs/1366722164

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