Harnessing the power of Topological Data Analysis to detect change points in time series
We introduce a novel geometry-oriented methodology, based on the emerging tools of topological data analysis, into the change point detection framework. The key rationale is that change points are likely to be associated with changes in geometry behind the data generating process. While the applicat...
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Online Access: | https://dx.doi.org/10.48550/arxiv.1910.12939 https://arxiv.org/abs/1910.12939 |
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ftdatacite:10.48550/arxiv.1910.12939 2023-05-15T17:33:02+02:00 Harnessing the power of Topological Data Analysis to detect change points in time series Islambekov, Umar Yuvaraj, Monisha Gel, Yulia R. 2019 https://dx.doi.org/10.48550/arxiv.1910.12939 https://arxiv.org/abs/1910.12939 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Machine Learning stat.ML Machine Learning cs.LG FOS Computer and information sciences Article CreativeWork article Preprint 2019 ftdatacite https://doi.org/10.48550/arxiv.1910.12939 2022-03-10T16:27:27Z We introduce a novel geometry-oriented methodology, based on the emerging tools of topological data analysis, into the change point detection framework. The key rationale is that change points are likely to be associated with changes in geometry behind the data generating process. While the applications of topological data analysis to change point detection are potentially very broad, in this paper we primarily focus on integrating topological concepts with the existing nonparametric methods for change point detection. In particular, the proposed new geometry-oriented approach aims to enhance detection accuracy of distributional regime shift locations. Our simulation studies suggest that integration of topological data analysis with some existing algorithms for change point detection leads to consistently more accurate detection results. We illustrate our new methodology in application to the two closely related environmental time series datasets -ice phenology of the Lake Baikal and the North Atlantic Oscillation indices, in a research query for a possible association between their estimated regime shift locations. : 11 pages, 3 Figures, 4 tables Article in Journal/Newspaper North Atlantic North Atlantic oscillation DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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unknown |
topic |
Machine Learning stat.ML Machine Learning cs.LG FOS Computer and information sciences |
spellingShingle |
Machine Learning stat.ML Machine Learning cs.LG FOS Computer and information sciences Islambekov, Umar Yuvaraj, Monisha Gel, Yulia R. Harnessing the power of Topological Data Analysis to detect change points in time series |
topic_facet |
Machine Learning stat.ML Machine Learning cs.LG FOS Computer and information sciences |
description |
We introduce a novel geometry-oriented methodology, based on the emerging tools of topological data analysis, into the change point detection framework. The key rationale is that change points are likely to be associated with changes in geometry behind the data generating process. While the applications of topological data analysis to change point detection are potentially very broad, in this paper we primarily focus on integrating topological concepts with the existing nonparametric methods for change point detection. In particular, the proposed new geometry-oriented approach aims to enhance detection accuracy of distributional regime shift locations. Our simulation studies suggest that integration of topological data analysis with some existing algorithms for change point detection leads to consistently more accurate detection results. We illustrate our new methodology in application to the two closely related environmental time series datasets -ice phenology of the Lake Baikal and the North Atlantic Oscillation indices, in a research query for a possible association between their estimated regime shift locations. : 11 pages, 3 Figures, 4 tables |
format |
Article in Journal/Newspaper |
author |
Islambekov, Umar Yuvaraj, Monisha Gel, Yulia R. |
author_facet |
Islambekov, Umar Yuvaraj, Monisha Gel, Yulia R. |
author_sort |
Islambekov, Umar |
title |
Harnessing the power of Topological Data Analysis to detect change points in time series |
title_short |
Harnessing the power of Topological Data Analysis to detect change points in time series |
title_full |
Harnessing the power of Topological Data Analysis to detect change points in time series |
title_fullStr |
Harnessing the power of Topological Data Analysis to detect change points in time series |
title_full_unstemmed |
Harnessing the power of Topological Data Analysis to detect change points in time series |
title_sort |
harnessing the power of topological data analysis to detect change points in time series |
publisher |
arXiv |
publishDate |
2019 |
url |
https://dx.doi.org/10.48550/arxiv.1910.12939 https://arxiv.org/abs/1910.12939 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1910.12939 |
_version_ |
1766131396231823360 |