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Rough-Fuzzy CPD: a gradual change point detection algorithm
Changepoint detection is the problem of finding abrupt or gradual changes in time series data when the distribution of the time series changes significantly. There are many sophisticated statistical algorithms for solving changepoint detection problem, although there is not much work devoted towards...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9664759/ http://dx.doi.org/10.1007/s42488-022-00077-3 |
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author | Bhaduri, Ritwik Roy, Subhrajyoty Pal, Sankar K. |
author_facet | Bhaduri, Ritwik Roy, Subhrajyoty Pal, Sankar K. |
author_sort | Bhaduri, Ritwik |
collection | PubMed |
description | Changepoint detection is the problem of finding abrupt or gradual changes in time series data when the distribution of the time series changes significantly. There are many sophisticated statistical algorithms for solving changepoint detection problem, although there is not much work devoted towards gradual changepoints as compared to abrupt ones. Here we present a new approach to solve the changepoint detection problem using the fuzzy rough set theory which is able to detect such gradual changepoints. An expression for the rough-fuzzy estimate of changepoints is derived along with its mathematical properties concerning fast computation. In a statistical hypothesis testing framework, the asymptotic distribution of the proposed statistic on both single and multiple changepoints is derived under the null hypothesis enabling multiple changepoint detection. Extensive simulation studies have been performed to investigate how simple crude statistical measures of disparity can be subjected to improve their efficiency in the estimation of gradual changepoints. Also, the said rough-fuzzy estimate is robust to signal-to-noise ratio, a high degree of fuzziness in true changepoints, and also to hyperparameter values. Simulation studies reveal that the proposed method beats other methods of gradual changepoint detection (including MJPD, HSMUCE, fuzzy methods like FCP, FCMLCP etc) and also popular crisp methods like Binary Segmentation, PELT, and BOCD in detecting gradual changepoints. The applicability of the estimate is demonstrated using multiple real-life datasets including Covid-19. We have developed the python package roufcp for broader dissemination of the methods. |
format | Online Article Text |
id | pubmed-9664759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-96647592022-11-14 Rough-Fuzzy CPD: a gradual change point detection algorithm Bhaduri, Ritwik Roy, Subhrajyoty Pal, Sankar K. J. of Data, Inf. and Manag. Original Article Changepoint detection is the problem of finding abrupt or gradual changes in time series data when the distribution of the time series changes significantly. There are many sophisticated statistical algorithms for solving changepoint detection problem, although there is not much work devoted towards gradual changepoints as compared to abrupt ones. Here we present a new approach to solve the changepoint detection problem using the fuzzy rough set theory which is able to detect such gradual changepoints. An expression for the rough-fuzzy estimate of changepoints is derived along with its mathematical properties concerning fast computation. In a statistical hypothesis testing framework, the asymptotic distribution of the proposed statistic on both single and multiple changepoints is derived under the null hypothesis enabling multiple changepoint detection. Extensive simulation studies have been performed to investigate how simple crude statistical measures of disparity can be subjected to improve their efficiency in the estimation of gradual changepoints. Also, the said rough-fuzzy estimate is robust to signal-to-noise ratio, a high degree of fuzziness in true changepoints, and also to hyperparameter values. Simulation studies reveal that the proposed method beats other methods of gradual changepoint detection (including MJPD, HSMUCE, fuzzy methods like FCP, FCMLCP etc) and also popular crisp methods like Binary Segmentation, PELT, and BOCD in detecting gradual changepoints. The applicability of the estimate is demonstrated using multiple real-life datasets including Covid-19. We have developed the python package roufcp for broader dissemination of the methods. Springer International Publishing 2022-11-15 2022 /pmc/articles/PMC9664759/ http://dx.doi.org/10.1007/s42488-022-00077-3 Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Bhaduri, Ritwik Roy, Subhrajyoty Pal, Sankar K. Rough-Fuzzy CPD: a gradual change point detection algorithm |
title | Rough-Fuzzy CPD: a gradual change point detection algorithm |
title_full | Rough-Fuzzy CPD: a gradual change point detection algorithm |
title_fullStr | Rough-Fuzzy CPD: a gradual change point detection algorithm |
title_full_unstemmed | Rough-Fuzzy CPD: a gradual change point detection algorithm |
title_short | Rough-Fuzzy CPD: a gradual change point detection algorithm |
title_sort | rough-fuzzy cpd: a gradual change point detection algorithm |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9664759/ http://dx.doi.org/10.1007/s42488-022-00077-3 |
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