# Unsupervised Change Detection using DRE-CUSUM

arXiv: https://arxiv.org/abs/2201.11678 · HTML: https://mlsanity.com/paper/2201.11678/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 8 of 8)

- [Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding](https://mlsanity.com/paper/2011.14097/) (2020)
- [Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergences](https://mlsanity.com/paper/1806.02261/) (2018)
- [Trimmed Density Ratio Estimation](https://mlsanity.com/paper/1703.03216/) (2017)
- [Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation](https://mlsanity.com/paper/2001.06386/) (2020)
- [Selective review of offline change point detection methods](https://mlsanity.com/paper/1801.00718/) (2018)
- [Differentially Private Change-Point Detection](https://mlsanity.com/paper/1808.10056/) (2018)
- [Constructive Setting of the Density Ratio Estimation Problem and its Rigorous Solution](https://mlsanity.com/paper/1306.0407/) (2013)
- [A clustering approach to heterogeneous change detection](https://mlsanity.com/paper/1702.03176/) (2017)

## Built on by (most cited first, 1 of 1)

- [Learning Fair Classifiers via Min-Max F-divergence Regularization](https://mlsanity.com/paper/2306.16552/) (2023)

## Abstract (arXiv)

This paper presents DRE-CUSUM, an unsupervised density-ratio estimation (DRE) based approach to determine statistical changes in time-series data when no knowledge of the pre-and post-change distributions are available. The core idea behind the proposed approach is to split the time-series at an arbitrary point and estimate the ratio of densities of distribution (using a parametric model such as a neural network) before and after the split point. The DRE-CUSUM change detection statistic is then derived from the cumulative sum (CUSUM) of the logarithm of the estimated density ratio. We present a theoretical justification as well as accuracy guarantees which show that the proposed statistic can reliably detect statistical changes, irrespective of the split point. While there have been prior works on using density ratio based methods for change detection, to the best of our knowledge, this is the first unsupervised change detection approach with a theoretical justification and accuracy guarantees. The simplicity of the proposed framework makes it readily applicable in various practical settings (including high-dimensional time-series data); we also discuss generalizations for online change detection. We experimentally show the superiority of DRE-CUSUM using both synthetic and real-world datasets over existing state-of-the-art unsupervised algorithms (such as Bayesian online change detection, its variants as well as several other heuristic methods).