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Title: An encoding approach for stable change point detection
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.  more » « less
Award ID(s):
1934568
PAR ID:
10555969
Author(s) / Creator(s):
;
Publisher / Repository:
Springer Nature
Date Published:
Journal Name:
Machine Learning
Volume:
113
Issue:
7
ISSN:
0885-6125
Page Range / eLocation ID:
4133 to 4163
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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