Accounting for Autocorrelation in Detecting Mean Shifts in Climate Data Series Using the Penalized Maximal t or F Test
Accounting for Autocorrelation in Detecting Mean Shifts in Climate Data Series Using the Penalized Maximal t or F Test
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DOI：
10.1175/2008jamc1741.1
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发表时间：
2008-09
影响因子：
3
通讯作者：
Xiaolan L. Wang
中科院分区：
文献类型：
--
作者：
Xiaolan L. Wang
摘要 本研究提出了一种经验方法，使用惩罚最大 t 检验或惩罚最大 F 检验来解释检测白色或红色（一阶自回归）高斯噪声时间序列中的均值漂移时的 lag-1 自相关性。这种经验方法被嵌入到逐步测试算法中，以便新算法可用于检测时间序列中的单个或多个变化点。通过蒙特卡罗模拟分析了新算法的检测能力。事实证明，新算法在检测单个或多个变化点方面工作得非常好且快速。介绍了它们在真实气候数据系列（表面压力和风速）中的应用示例。用于实现算法的开源软件包（R 和 FORTRAN 语言）以及用户手册已经开发出来并免费在线提供。
Abstract This study proposes an empirical approach to account for lag-1 autocorrelation in detecting mean shifts in time series of white or red (first-order autoregressive) Gaussian noise using the penalized maximal t test or the penalized maximal F test. This empirical approach is embedded in a stepwise testing algorithm, so that the new algorithms can be used to detect single or multiple changepoints in a time series. The detection power of the new algorithms is analyzed through Monte Carlo simulations. It has been shown that the new algorithms work very well and fast in detecting single or multiple changepoints. Examples of their application to real climate data series (surface pressure and wind speed) are presented. An open-source software package (in R and FORTRAN) for implementing the algorithms, along with a user manual, has been developed and made available online free of charge.