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WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection

arXiv 2506.08066 ↗First posted 9 Jun 2025stat.ML cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 59 references that are in the map. First posted on arXiv 9 Jun 2025.

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References · 30

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18 more
  1. 2020Improving model calibration with accuracy versus uncertainty optimizationcited by 123
  2. 2023Calibration in Deep Learning: A Survey of the State-of-the-Artcited by 72
  3. 2020An Evaluation of Change Point Detection Algorithmscited by 62
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