WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909819730919424 |
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| author | Stepikin, Alexander Romanenkova, Evgenia Zaytsev, Alexey |
| author_facet | Stepikin, Alexander Romanenkova, Evgenia Zaytsev, Alexey |
| contents | Change Point Detection (CPD) aims to identify moments of abrupt distribution shifts in data streams. Real-world high-dimensional CPD remains challenging due to data pattern complexity and violation of common assumptions. Resorting to standalone deep neural networks, the current state-of-the-art detectors have yet to achieve perfect quality. Concurrently, ensembling provides more robust solutions, boosting the performance. In this paper, we investigate ensembles of deep change point detectors and realize that standard prediction aggregation techniques, e.g., averaging, are suboptimal and fail to account for problem peculiarities. Alternatively, we introduce WWAggr -- a novel task-specific method of ensemble aggregation based on the Wasserstein distance. Our procedure is versatile, working effectively with various ensembles of deep CPD models. Moreover, unlike existing solutions, we practically lift a long-standing problem of the decision threshold selection for CPD. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_08066 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection Stepikin, Alexander Romanenkova, Evgenia Zaytsev, Alexey Machine Learning Artificial Intelligence Change Point Detection (CPD) aims to identify moments of abrupt distribution shifts in data streams. Real-world high-dimensional CPD remains challenging due to data pattern complexity and violation of common assumptions. Resorting to standalone deep neural networks, the current state-of-the-art detectors have yet to achieve perfect quality. Concurrently, ensembling provides more robust solutions, boosting the performance. In this paper, we investigate ensembles of deep change point detectors and realize that standard prediction aggregation techniques, e.g., averaging, are suboptimal and fail to account for problem peculiarities. Alternatively, we introduce WWAggr -- a novel task-specific method of ensemble aggregation based on the Wasserstein distance. Our procedure is versatile, working effectively with various ensembles of deep CPD models. Moreover, unlike existing solutions, we practically lift a long-standing problem of the decision threshold selection for CPD. |
| title | WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.08066 |