# Predictive change point detection for heterogeneous data

arXiv: https://arxiv.org/abs/2305.06630 · HTML: https://mlsanity.com/paper/2305.06630/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 67 references that are in the map.
- First posted on arXiv 11 May 2023.

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

- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [High-dimensional, multiscale online changepoint detection](https://mlsanity.com/paper/2003.03668/) (2020)
- [Robust Sequential Change-Point Detection by Convex Optimization](https://mlsanity.com/paper/1701.06952/) (2017)
- [Detecting changes in slope with an L0 penalty](https://mlsanity.com/paper/1701.01672/) (2017)
- [Are deviations in a gradually varying mean relevant? A testing approach based on sup-norm estimators](https://mlsanity.com/paper/2002.06143/) (2020)
- [Automatic Learning to Detect Concept Drift](https://mlsanity.com/paper/2105.01419/) (2021)
- [Online Changepoint Detection on a Budget](https://mlsanity.com/paper/2201.03710/) (2022)
- [Detecting gradual changes in locally stationary processes](https://mlsanity.com/paper/1310.4678/) (2013)

## Abstract (arXiv)

A change point detection (CPD) framework assisted by a predictive machine learning model called "Predict and Compare" is introduced and characterised in relation to other state-of-the-art online CPD routines which it outperforms in terms of false positive rate and out-of-control average run length. The method's focus is on improving standard methods from sequential analysis such as the CUSUM rule in terms of these quality measures. This is achieved by replacing typically used trend estimation functionals such as the running mean with more sophisticated predictive models (Predict step), and comparing their prognosis with actual data (Compare step). The two models used in the Predict step are the ARIMA model and the LSTM recursive neural network. However, the framework is formulated in general terms, so as to allow the use of other prediction or comparison methods than those tested here. The power of the method is demonstrated in a tribological case study in which change points separating the run-in, steady-state, and divergent wear phases are detected in the regime of very few false positives.