# Online Changepoint Detection on a Budget

arXiv: https://arxiv.org/abs/2201.03710 · HTML: https://mlsanity.com/paper/2201.03710/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 11 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1309.3295/) (2013)
- [Tiny Machine Learning for Concept Drift](https://mlsanity.com/paper/2107.14759/) (2021)
- [Asynchronous Multi-Sensor Change-Point Detection for Seismic Tremors](https://mlsanity.com/paper/1901.08196/) (2019)

## Built on by (most cited first, 3 of 3)

- [Generalized Event Cameras](https://mlsanity.com/paper/2407.02683/) (2024)
- [Predictive change point detection for heterogeneous data](https://mlsanity.com/paper/2305.06630/) (2023)
- [Anomalous Change Point Detection Using Probabilistic Predictive Coding](https://mlsanity.com/paper/2405.15727/) (2024)

## Abstract (arXiv)

Changepoints are abrupt variations in the underlying distribution of data. Detecting changes in a data stream is an important problem with many applications. In this paper, we are interested in changepoint detection algorithms which operate in an online setting in the sense that both its storage requirements and worst-case computational complexity per observation are independent of the number of previous observations. We propose an online changepoint detection algorithm for both univariate and multivariate data which compares favorably with offline changepoint detection algorithms while also operating in a strictly more constrained computational model. In addition, we present a simple online hyperparameter auto tuning technique for these algorithms.