# Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Data

arXiv: https://arxiv.org/abs/2503.09541 · HTML: https://mlsanity.com/paper/2503.09541/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Nonparametric regression using deep neural networks with ReLU activation function](https://mlsanity.com/paper/1708.06633/) (2017)
- [Statistically Significant Detection of Linguistic Change](https://mlsanity.com/paper/1411.3315/) (2014)
- [Kernel Change-point Detection with Auxiliary Deep Generative Models](https://mlsanity.com/paper/1901.06077/) (2019)
- [Multiple-change-point detection for high dimensional time series via sparsified binary segmentation](https://mlsanity.com/paper/1611.08639/) (2016)
- [Theoretical analysis of deep neural networks for temporally dependent observations](https://mlsanity.com/paper/2210.11530/) (2022)
- [Random Forests for Change Point Detection](https://mlsanity.com/paper/2205.04997/) (2022)
- [ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1309.3295/) (2013)
- [Automatic Change-Point Detection in Time Series via Deep Learning](https://mlsanity.com/paper/2211.03860/) (2022)
- [Change Point Estimation in a Dynamic Stochastic Block Model](https://mlsanity.com/paper/1812.03090/) (2018)
- [Change-point detection in panel data via double CUSUM statistic](https://mlsanity.com/paper/1611.08631/) (2016)
- [Sequential Changepoint Detection in Neural Networks with Checkpoints](https://mlsanity.com/paper/2010.03053/) (2020)

## Abstract (arXiv)

The paper studies the problem of detecting and locating change points in multivariate time-evolving data. The problem has a long history in statistics and signal processing and various algorithms have been developed primarily for simple parametric models. In this work, we focus on modeling the data through feed-forward neural networks and develop a detection strategy based on the following two-step procedure. In the first step, the neural network is trained over a prespecified window of the data, and its test error function is calibrated over another prespecified window. Then, the test error function is used over a moving window to identify the change point. Once a change point is detected, the procedure involving these two steps is repeated until all change points are identified. The proposed strategy yields consistent estimates for both the number and the locations of the change points under temporal dependence of the data-generating process. The effectiveness of the proposed strategy is illustrated on synthetic data sets that provide insights on how to select in practice tuning parameters of the algorithm and in real data sets. Finally, we note that although the detection strategy is general and can work with different neural network architectures, the theoretical guarantees provided are specific to feed-forward neural architectures.