# NEWMA: a new method for scalable model-free online change-point detection

arXiv: https://arxiv.org/abs/1805.08061 · HTML: https://mlsanity.com/paper/1805.08061/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2018.

## Built on by (most cited first, 13 of 13)

- [Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness](https://mlsanity.com/paper/2109.14120/) (2021)
- [A Log-Linear Non-Parametric Online Changepoint Detection Algorithm based on Functional Pruning](https://mlsanity.com/paper/2302.02718/) (2023)
- [Maximum Mean Discrepancy on Exponential Windows for Online Change Detection](https://mlsanity.com/paper/2205.12706/) (2022)
- [Nonparametric and Online Change Detection in Multivariate Datastreams using QuantTree](https://mlsanity.com/paper/2208.14801/) (2022)
- [Raising the ClaSS of Streaming Time Series Segmentation](https://mlsanity.com/paper/2310.20431/) (2023)
- [A Constant-per-Iteration Likelihood Ratio Test for Online Changepoint Detection for Exponential Family Models](https://mlsanity.com/paper/2302.04743/) (2023)
- [Optimal Online Change Detection via Random Fourier Features](https://mlsanity.com/paper/2505.17789/) (2025)
- [Class Distribution Monitoring for Concept Drift Detection](https://mlsanity.com/paper/2210.08470/) (2022)
- [Greedy online change point detection](https://mlsanity.com/paper/2308.07012/) (2023)
- [RIO-CPD: A Riemannian Geometric Method for Correlation-aware Online Change Point Detection](https://mlsanity.com/paper/2407.09698/) (2024)
- [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)
- [Riemannian Change Point Detection on Manifolds with Robust Centroid Estimation](https://mlsanity.com/paper/2508.18045/) (2025)

## Abstract (arXiv)

We consider the problem of detecting abrupt changes in the distribution of a multi-dimensional time series, with limited computing power and memory. In this paper, we propose a new, simple method for model-free online change-point detection that relies only on fast and light recursive statistics, inspired by the classical Exponential Weighted Moving Average algorithm (EWMA). The proposed idea is to compute two EWMA statistics on the stream of data with different forgetting factors, and to compare them. By doing so, we show that we implicitly compare recent samples with older ones, without the need to explicitly store them. Additionally, we leverage Random Features (RFs) to efficiently use the Maximum Mean Discrepancy as a distance between distributions, furthermore exploiting recent optical hardware to compute high-dimensional RFs in near constant time. We show that our method is significantly faster than usual non-parametric methods for a given accuracy.