Detecting Abrupt Changes in a Piecewise Locally Stationary Time Series.
Detecting Abrupt Changes in a Piecewise Locally Stationary Time Series.
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DOI：
10.1016/j.jmva.2007.06.010
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发表时间：
2008-02
影响因子：
1.6
通讯作者：
M. Last;R. Shumway
中科院分区：
文献类型：
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作者：
M. Last;R. Shumway
非平稳时间序列出现在许多环境中，如地震学，语音处理和金融。在许多这些设置中，我们感兴趣的是局部平稳性模型被违反的点。我们考虑的问题是如何检测这些变点，我们确定通过发现急剧变化的时变功率谱。几种不同的方法被认为是，我们发现，对称Kullback-Leibler信息歧视进行最好的模拟研究。我们推导出我们的检验统计量的渐近正态性，估计变点位置的一致性。然后，我们展示了在地震中检测到达相位的问题上的技术。
Non-stationary time series arise in many settings, such as seismology, speech-processing, and finance. In many of these settings we are interested in points where a model of local stationarity is violated. We consider the problem of how to detect these change-points, which we identify by finding sharp changes in the time-varying power spectrum. Several different methods are considered, and we find that the symmetrized Kullback–Leibler information discrimination performs best in simulation studies. We derive asymptotic normality of our test statistic, and consistency of estimated change-point locations. We then demonstrate the technique on the problem of detecting arrival phases in earthquakes.