# Dynamic Interpretable Change Point Detection

arXiv: https://arxiv.org/abs/2211.03991 · HTML: https://mlsanity.com/paper/2211.03991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [MMD Aggregated Two-Sample Test](https://mlsanity.com/paper/2110.15073/) (2021)
- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding](https://mlsanity.com/paper/2011.14097/) (2020)
- [Network Inference via the Time-Varying Graphical Lasso](https://mlsanity.com/paper/1703.01958/) (2017)
- [Kernel Change-point Detection with Auxiliary Deep Generative Models](https://mlsanity.com/paper/1901.06077/) (2019)
- [Scan B-Statistic for Kernel Change-Point Detection](https://mlsanity.com/paper/1507.01279/) (2015)
- [A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1306.4933/) (2013)
- [Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation](https://mlsanity.com/paper/2008.09524/) (2020)
- [Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation](https://mlsanity.com/paper/2001.06386/) (2020)
- [Optimal Transport Based Change Point Detection and Time Series Segment Clustering](https://mlsanity.com/paper/1911.01325/) (2019)
- [Change-Point Estimation in High-Dimensional Markov Random Field Models](https://mlsanity.com/paper/1405.6176/) (2014)

## Abstract (arXiv)

Identifying change points (CPs) in a time series is crucial to guide better decision making across various fields like finance and healthcare and facilitating timely responses to potential risks or opportunities. Existing Change Point Detection (CPD) methods have a limitation in tracking changes in the joint distribution of multidimensional features. In addition, they fail to generalize effectively within the same time series as different types of CPs may require different detection methods. As the volume of multidimensional time series continues to grow, capturing various types of complex CPs such as changes in the correlation structure of the time-series features has become essential. To overcome the limitations of existing methods, we propose TiVaCPD, an approach that uses a Time-Varying Graphical Lasso (TVGL) to identify changes in correlation patterns between multidimensional features over time, and combines that with an aggregate Kernel Maximum Mean Discrepancy (MMD) test to identify changes in the underlying statistical distributions of dynamic time windows with varying length. The MMD and TVGL scores are combined using a novel ensemble method based on similarity measures leveraging the power of both statistical tests. We evaluate the performance of TiVaCPD in identifying and characterizing various types of CPs and show that our method outperforms current state-of-the-art methods in real-world CPD datasets. We further demonstrate that TiVaCPD scores characterize the type of CPs and facilitate interpretation of change dynamics, offering insights into real-life applications.