An encoding approach for stable change point detection

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiaodong Wang, Fushing Hsieh
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引用次数: 0

Abstract

Without imposing prior distributional knowledge underlying multivariate time series of interest, we propose a nonparametric change-point detection approach to estimate the number of change points and their locations along the temporal axis. We develop a structural subsampling procedure such that the observations are encoded into multiple sequences of Bernoulli variables. A maximum likelihood approach in conjunction with a newly developed searching algorithm is implemented to detect change points on each Bernoulli process separately. Then, aggregation statistics are proposed to collectively synthesize change-point results from all individual univariate time series into consistent and stable location estimations. We also study a weighting strategy to measure the degree of relevance for different subsampled groups. Simulation studies are conducted and shown that the proposed change-point methodology for multivariate time series has favorable performance comparing with currently available state-of-the-art nonparametric methods under various settings with different degrees of complexity. Real data analyses are finally performed on categorical, ordinal, and continuous time series taken from fields of genetics, climate, and finance.

Abstract Image

稳定变化点检测编码方法
我们提出了一种非参数变化点检测方法,在不强加相关多元时间序列的先验分布知识的情况下,估计变化点的数量及其沿时间轴的位置。我们开发了一种结构性子采样程序,将观测数据编码为多个伯努利变量序列。最大似然法与新开发的搜索算法相结合,分别检测每个伯努利过程的变化点。然后,我们提出了聚合统计方法,将所有单变量时间序列的变化点结果综合为一致且稳定的位置估计。我们还研究了一种加权策略,用于衡量不同子采样组的相关程度。我们进行了模拟研究,结果表明,在复杂程度不同的各种设置下,针对多变量时间序列提出的变化点方法与目前可用的最先进的非参数方法相比,具有良好的性能。最后对遗传学、气候和金融领域的分类、序数和连续时间序列进行了真实数据分析。
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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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