{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/change-point-detection-on-hierarchical","title":"Change-Point Detection on Hierarchical Circadian Models","arxiv_id":"1809.04197","date":"2018-09-11","proceeding":null,"authors":["Pablo Moreno-Muñoz","David Ramírez","Antonio Artés-Rodríguez"],"abstract":"This paper addresses the problem of change-point detection on sequences of\nhigh-dimensional and heterogeneous observations, which also possess a periodic\ntemporal structure. Due to the dimensionality problem, when the time between\nchange-points is on the order of the dimension of the model parameters, drifts\nin the underlying distribution can be misidentified as changes. To overcome\nthis limitation, we assume that the observations lie in a lower-dimensional\nmanifold that admits a latent variable representation. In particular, we\npropose a hierarchical model that is computationally feasible, widely\napplicable to heterogeneous data and robust to missing instances. Additionally,\nthe observations' periodic dependencies are captured by non-stationary periodic\ncovariance functions. The proposed technique is particularly fitted to (and\nmotivated by) the problem of detecting changes in human behavior using\nsmartphones and its application to relapse detection in psychiatric patients.\nFinally, we validate the technique on synthetic examples and we demonstrate its\nutility in the detection of behavioral changes using real data acquired by\nsmartphones.","url_abs":"http://arxiv.org/abs/1809.04197v2","url_pdf":"http://arxiv.org/pdf/1809.04197v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"change-point-detection-on-hierarchical","repo_url":"https://github.com/pmorenoz/HierCPD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"change-point-detection","task_name":"Change Point Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.04197","atlas_url":"https://app.syntology.ai/?focus=1809.04197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.04197"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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