Detecting linear trend changes in data sequences
Detecting linear trend changes in data sequences
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DOI：
10.1007/s00362-023-01458-5
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发表时间：
2023-06-22
影响因子：
1.3
通讯作者：
Fryzlewicz，Piotr
中科院分区：
文献类型：
--
作者：
Maeng，Hyeyoung;Fryzlewicz，Piotr
我们提出了TrendSegment，一种检测一维数据中对应线性趋势变化的多个变化点的方法。TrendSegment的核心成分是一种新的尾贪婪不平衡小波变换：通过自适应构造的不平衡小波基对数据进行条件正交、自下而上的变换，从而得到数据的稀疏表示。由于其自下而上的性质，这种多尺度分解在早期阶段关注局部特征，然后关注全局特征，从而可以同时检测长和短线性趋势段。为了降低计算复杂度，本文提出的方法在一次数据传递中合并多个区域。我们展示了变更点的估计数量和位置的一致性。通过模拟和两个实际数据实例，包括冰岛的温度数据和北极和南极的海冰范围，证明了我们方法的实用性。我们的方法是在R包etrendsegmentr中实现的，可以从CRAN获得。
We propose TrendSegment, a methodology for detecting multiple change-points corresponding to linear trend changes in one dimensional data. A core ingredient of TrendSegment is a new Tail-Greedy Unbalanced Wavelet transform: a conditionally orthonormal, bottom-up transformation of the data through an adaptively constructed unbalanced wavelet basis, which results in a sparse representation of the data. Due to its bottom-up nature, this multiscale decomposition focuses on local features in its early stages and on global features next which enables the detection of both long and short linear trend segments at once. To reduce the computational complexity, the proposed method merges multiple regions in a single pass over the data. We show the consistency of the estimated number and locations of change-points. The practicality of our approach is demonstrated through simulations and two real data examples, involving Iceland temperature data and sea ice extent of the Arctic and the Antarctic. Our methodology is implemented in the R packagetrendsegmentR, available from CRAN.