Changepoint detection in periodic and autocorrelated time series
Changepoint detection in periodic and autocorrelated time series
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DOI：
10.1175/jcli4291.1
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发表时间：
2007-10-15
影响因子：
4.9
通讯作者：
Feng, Yang
中科院分区：
文献类型：
--
作者：
Lund, Robert;Wang, Xiaolan L.;Feng, Yang
气候时间序列中普遍存在着未记录的变点（不均匀性）。时间序列中由变点引起的水平位移是一种非常重要的数据特征，它混淆了许多推理问题。对于来自具有独立同分布误差的模型的未记录的变点的测试，现在已经很好地理解了。然而，大多数气候序列表现出序列自相关。月、日或小时序列也可能有周期性的平均结构。本文开发了一个针对周期性和自相关时间序列的未记录变点的测试。经典的变点检验的基础上的平方误差的总和进行修改，以考虑到系列自相关和周期性。应用该方法对两个气候序列进行了分析。
Undocumented changepoints ( inhomogeneities) are ubiquitous features of climatic time series. Level shifts in time series caused by changepoints confound many inference problems and are very important data features. Tests for undocumented changepoints from models that have independent and identically distributed errors are by now well understood. However, most climate series exhibit serial autocorrelation. Monthly, daily, or hourly series may also have periodic mean structures. This article develops a test for undocumented changepoints for periodic and autocorrelated time series. Classical changepoint tests based on sums of squared errors are modified to take into account series autocorrelations and periodicities. The methods are applied in the analyses of two climate series.