# Change Detection in Multivariate data streams: Online Analysis with Kernel-QuantTree

arXiv: https://arxiv.org/abs/2410.13778 · HTML: https://mlsanity.com/paper/2410.13778/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [NEWMA: a new method for scalable model-free online change-point detection](https://mlsanity.com/paper/1805.08061/) (2018)
- [Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss](https://mlsanity.com/paper/1510.04850/) (2015)
- [Online Kernel CUSUM for Change-Point Detection](https://mlsanity.com/paper/2211.15070/) (2022)
- [Nonparametric and Online Change Detection in Multivariate Datastreams using QuantTree](https://mlsanity.com/paper/2208.14801/) (2022)

## Abstract (arXiv)

We present Kernel-QuantTree Exponentially Weighted Moving Average (KQT-EWMA), a non-parametric change-detection algorithm that combines the Kernel-QuantTree (KQT) histogram and the EWMA statistic to monitor multivariate data streams online. The resulting monitoring scheme is very flexible, since histograms can be used to model any stationary distribution, and practical, since the distribution of test statistics does not depend on the distribution of datastream in stationary conditions (non-parametric monitoring). KQT-EWMA enables controlling false alarms by operating at a pre-determined Average Run Length (ARL0), which measures the expected number of stationary samples to be monitored before triggering a false alarm. The latter peculiarity is in contrast with most non-parametric change-detection tests, which rarely can control the ARL0 a priori. Our experiments on synthetic and real-world datasets demonstrate that KQT-EWMA can control ARL0 while achieving detection delays comparable to or lower than state-of-the-art methods designed to work in the same conditions.