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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.14798 |
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| _version_ | 1866913277853827072 |
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| author | Waltz, Nicholas |
| author_facet | Waltz, Nicholas |
| contents | Economic policy and research rely on the correct evaluation of the billions of high-frequency data points that we collect every day. Consistent clustering algorithms, like DBSCAN, allow us to make sense of the data in a useful way. However, while there is a large literature on the consistency of various clustering algorithms for high-dimensional static clustering, the literature on multivariate time series clustering still largely relies on heuristics or restrictive assumptions. The aim of this paper is to prove a notion of consistency of DBSCAN for the task of clustering multivariate time series. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14798 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Time Series Clustering Using DBSCAN Waltz, Nicholas Statistics Theory Economic policy and research rely on the correct evaluation of the billions of high-frequency data points that we collect every day. Consistent clustering algorithms, like DBSCAN, allow us to make sense of the data in a useful way. However, while there is a large literature on the consistency of various clustering algorithms for high-dimensional static clustering, the literature on multivariate time series clustering still largely relies on heuristics or restrictive assumptions. The aim of this paper is to prove a notion of consistency of DBSCAN for the task of clustering multivariate time series. |
| title | Time Series Clustering Using DBSCAN |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2403.14798 |