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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.<\/jats:p>","DOI":"10.1007\/s10994-023-06510-x","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T23:01:46Z","timestamp":1709161306000},"page":"4133-4163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An encoding approach for stable change point detection"],"prefix":"10.1007","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2330-8660","authenticated-orcid":false,"given":"Xiaodong","family":"Wang","sequence":"first","affiliation":[]},{"given":"Fushing","family":"Hsieh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"issue":"162","key":"6510_CR1","first-page":"1","volume":"20","author":"S Arlot","year":"2019","unstructured":"Arlot, S., Celisse, A., & Harchaoui, Z. 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