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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2210.09817 |
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| _version_ | 1866915939621011456 |
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| author | Pineau, Edouard Razakarivony, Sébastien |
| author_facet | Pineau, Edouard Razakarivony, Sébastien |
| contents | In this paper, we describe a universal method for extracting the underlying monotonic trend factor from time series data. We propose an approach related to the Mann-Kendall test, a standard monotonic trend detection method and call it contrastive trend estimation (CTE). We show that the CTE method identifies any hidden trend underlying temporal data while avoiding the standard assumptions used for monotonic trend identification. In particular, CTE can take any type of temporal data (vector, images, graphs, time series, etc.) as input. We finally illustrate the interest of our CTE method through several experiments on different types of data and problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_09817 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Universal hidden monotonic trend estimation with contrastive learning Pineau, Edouard Razakarivony, Sébastien Machine Learning In this paper, we describe a universal method for extracting the underlying monotonic trend factor from time series data. We propose an approach related to the Mann-Kendall test, a standard monotonic trend detection method and call it contrastive trend estimation (CTE). We show that the CTE method identifies any hidden trend underlying temporal data while avoiding the standard assumptions used for monotonic trend identification. In particular, CTE can take any type of temporal data (vector, images, graphs, time series, etc.) as input. We finally illustrate the interest of our CTE method through several experiments on different types of data and problems. |
| title | Universal hidden monotonic trend estimation with contrastive learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2210.09817 |