WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection
This work provides a domain-specific improvement for researchers and practitioners in change point detection, offering a more robust aggregation method for ensembles of deep models.
The paper tackled the problem of ensemble aggregation for change point detection in high-dimensional data streams, introducing WWAggr, a Wasserstein distance-based method that improved performance and addressed threshold selection, achieving unspecified gains over standard techniques.
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.