# High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability

arXiv: https://arxiv.org/abs/2609.24278 · HTML: https://mlsanity.com/paper/2609.24278/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 62 references that are in the map.
- First posted on arXiv 21 Sep 2026.

## Builds on (most cited first, 25 of 25)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances](https://mlsanity.com/paper/2210.09160/) (2022)
- [Kernel Change-point Detection with Auxiliary Deep Generative Models](https://mlsanity.com/paper/1901.06077/) (2019)
- [A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1306.4933/) (2013)
- [Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergences](https://mlsanity.com/paper/1806.02261/) (2018)
- [Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation](https://mlsanity.com/paper/2008.09524/) (2020)
- [ClaSP -- Parameter-free Time Series Segmentation](https://mlsanity.com/paper/2207.13987/) (2022)
- [Two-sample Test using Projected Wasserstein Distance](https://mlsanity.com/paper/2010.11970/) (2020)
- [ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1309.3295/) (2013)
- [Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection](https://mlsanity.com/paper/1805.05383/) (2018)
- [WATCH: Wasserstein Change Point Detection for High-Dimensional Time Series Data](https://mlsanity.com/paper/2201.07125/) (2022)
- [Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation](https://mlsanity.com/paper/2001.06386/) (2020)
- [Fast Online Changepoint Detection via Functional Pruning CUSUM statistics](https://mlsanity.com/paper/2110.08205/) (2021)
- [Optimal Transport Based Change Point Detection and Time Series Segment Clustering](https://mlsanity.com/paper/1911.01325/) (2019)
- [Online Neural Networks for Change-Point Detection](https://mlsanity.com/paper/2010.01388/) (2020)
- [Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions](https://mlsanity.com/paper/1405.5581/) (2014)
- [On Matched Filtering for Statistical Change Point Detection](https://mlsanity.com/paper/2006.05539/) (2020)
- [Sequential nonparametric tests for a change in distribution: an application to detecting radiological anomalies](https://mlsanity.com/paper/1612.07867/) (2016)

## Abstract (arXiv)

Change point detection (CPD) identifies abrupt and significant changes in sequential data, with applications in human activity recognition, financial markets, cybersecurity, manufacturing, and autonomous systems. Traditional CPD methods often face computational challenges in high-dimensional settings and typically provide limited explanations for detected changes, which can restrict their practical usability. This paper introduces a CPD framework that improves scalability and interpretability by leveraging the Sliced Wasserstein (SW) distance. Our contributions are fourfold: (1) we transform multivariate sequential data into one-dimensional scores using the SW distance, making the resulting representation compatible with existing CPD methods; (2) we analyze the distributional behavior of random slices of the SW distance and show that, under suitable assumptions, they can be approximated by a Gamma distribution, providing a principled basis for threshold calibration; (3) we propose a self-adapting online CPD algorithm that combines this SW-based score with an adaptive quantile-based threshold; (4) we introduce a model-specific framework for generating contrastive explanations for annotated change points. Empirically, our method reduces false positives by at least 48% on average compared with popular online and offline CPD baselines, while maintaining competitive or superior detection performance. Code is available at https://github.com/jsve96/SWCPDCode. At the same time, it produces interpretable change-point annotations, making it practical for deployment in high-stakes applications.