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

TL;DR

This paper introduces WWAggr, a novel Wasserstein-based ensemble aggregation method for change point detection that improves robustness and addresses threshold selection issues in high-dimensional data streams.

Contribution

We propose WWAggr, a task-specific ensemble aggregation technique based on Wasserstein distance, enhancing deep CPD model performance and threshold determination.

Findings

WWAggr outperforms standard averaging in ensemble CPD tasks.

The method effectively handles high-dimensional data streams.

WWAggr simplifies decision threshold selection.

Abstract

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.