A wavelet-based approach for detecting changes in second order structure within nonstationary time series
A wavelet-based approach for detecting changes in second order structure within nonstationary time series
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一种基于小波的方法，用于检测非平稳时间序列中二阶结构的变化
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
P. Jonathan
中科院分区：
文献类型：
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作者：
Rebecca Killick;I. Eckley;P. Jonathan
本文提出了一种检验非平稳时间序列中一般自协方差结构变化的方法。我们的方法是建立在局部平稳小波（LSW）的时间序列，以前已被用于时间序列的分类和分割过程模型。使用这个框架，我们形成了一个基于似然的假设检验，并证明其性能对现有的方法在各种模拟的例子，以及将其应用到海洋工程所产生的问题。
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.