# Nonparametric and Online Change Detection in Multivariate Datastreams using QuantTree

arXiv: https://arxiv.org/abs/2208.14801 · HTML: https://mlsanity.com/paper/2208.14801/

## Key facts

- Cited by 6 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 30 Aug 2022.

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

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [Scan B-Statistic for Kernel Change-Point Detection](https://mlsanity.com/paper/1507.01279/) (2015)
- [Neural Tangent Kernel Maximum Mean Discrepancy](https://mlsanity.com/paper/2106.03227/) (2021)
- [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)
- [Quickest Change Detection in Anonymous Heterogeneous Sensor Networks](https://mlsanity.com/paper/2202.13023/) (2022)
- [Multichannel Sequential Detection- Part I: Non-i.i.d. Data](https://mlsanity.com/paper/1601.03379/) (2016)

## Built on by (most cited first, 6 of 6)

- [Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection](https://mlsanity.com/paper/2603.06757/) (2026)
- [Real-time Fuel Leakage Detection via Online Change Point Detection](https://mlsanity.com/paper/2410.09741/) (2024)
- [Change Detection in Multivariate data streams: Online Analysis with Kernel-QuantTree](https://mlsanity.com/paper/2410.13778/) (2024)
- [Online Isolation Forest](https://mlsanity.com/paper/2505.09593/) (2025)
- [Explainable Condition Monitoring via Probabilistic Anomaly Detection Applied to Helicopter Transmissions](https://mlsanity.com/paper/2603.08130/) (2026)
- [Dimension-Calibrated Unexplained Mass: An Interpretable Drift Statistic for Contamination Monitoring in Data Streams](https://mlsanity.com/paper/2607.16811/) (2026)

## Abstract (arXiv)

We address the problem of online change detection in multivariate datastreams, and we introduce QuantTree Exponentially Weighted Moving Average (QT-EWMA), a nonparametric change-detection algorithm that can control the expected time before a false alarm, yielding a desired Average Run Length (ARL0). Controlling false alarms is crucial in many applications and is rarely guaranteed by online change-detection algorithms that can monitor multivariate datastreams without knowing the data distribution. Like many change-detection algorithms, QT-EWMA builds a model of the data distribution, in our case a QuantTree histogram, from a stationary training set. To monitor datastreams even when the training set is extremely small, we propose QT-EWMA-update, which incrementally updates the QuantTree histogram during monitoring, always keeping the ARL0 under control. Our experiments, performed on synthetic and real-world datastreams, demonstrate that QT-EWMA and QT-EWMA-update control the ARL0 and the false alarm rate better than state-of-the-art methods operating in similar conditions, achieving lower or comparable detection delays.