# Memory-free Online Change-point Detection: A Novel Neural Network Approach

arXiv: https://arxiv.org/abs/2207.03932 · HTML: https://mlsanity.com/paper/2207.03932/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 72 references that are in the map.
- First posted on arXiv 8 Jul 2022.

## Builds on (most cited first, 19 of 19)

- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection](https://mlsanity.com/paper/1607.00148/) (2016)
- [A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data](https://mlsanity.com/paper/1811.08055/) (2018)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [Continual Unsupervised Representation Learning](https://mlsanity.com/paper/1910.14481/) (2019)
- [Learning to Diagnose with LSTM Recurrent Neural Networks](https://mlsanity.com/paper/1511.03677/) (2015)
- [Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks](https://mlsanity.com/paper/2108.00298/) (2021)
- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding](https://mlsanity.com/paper/2011.14097/) (2020)
- [Kernel Change-point Detection with Auxiliary Deep Generative Models](https://mlsanity.com/paper/1901.06077/) (2019)
- [Scan B-Statistic for Kernel Change-Point Detection](https://mlsanity.com/paper/1507.01279/) (2015)
- [A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1306.4933/) (2013)
- [A Survey on Anomaly Detection for Technical Systems using LSTM Networks](https://mlsanity.com/paper/2105.13810/) (2021)
- [Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant Representation](https://mlsanity.com/paper/2008.09524/) (2020)
- [Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning](https://mlsanity.com/paper/2012.08101/) (2020)
- [Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection](https://mlsanity.com/paper/1805.05383/) (2018)
- [Active multi-fidelity Bayesian online changepoint detection](https://mlsanity.com/paper/2103.14224/) (2021)
- [Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series](https://mlsanity.com/paper/1905.06913/) (2019)

## Built on by (most cited first, 5 of 5)

- [Neural network-based CUSUM for online change-point detection](https://mlsanity.com/paper/2210.17312/) (2022)
- [Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection](https://mlsanity.com/paper/2401.03322/) (2024)
- [Anomalous Change Point Detection Using Probabilistic Predictive Coding](https://mlsanity.com/paper/2405.15727/) (2024)
- [Changepoint Detection in Highly-Attributed Dynamic Graphs](https://mlsanity.com/paper/2407.06998/) (2024)
- [Real-time Fuel Leakage Detection via Online Change Point Detection](https://mlsanity.com/paper/2410.09741/) (2024)

## Abstract (arXiv)

Change-point detection (CPD), which detects abrupt changes in the data distribution, is recognized as one of the most significant tasks in time series analysis. Despite the extensive literature on offline CPD, unsupervised online CPD still suffers from major challenges, including scalability, hyperparameter tuning, and learning constraints. To mitigate some of these challenges, in this paper, we propose a novel deep learning approach for unsupervised online CPD from multi-dimensional time series, named Adaptive LSTM-Autoencoder Change-Point Detection (ALACPD). ALACPD exploits an LSTM-autoencoder-based neural network to perform unsupervised online CPD. It continuously adapts to the incoming samples without keeping the previously received input, thus being memory-free. We perform an extensive evaluation on several real-world time series CPD benchmarks. We show that ALACPD, on average, ranks first among state-of-the-art CPD algorithms in terms of quality of the time series segmentation, and it is on par with the best performer in terms of the accuracy of the estimated change-points. The implementation of ALACPD is available online on Githubfootnoteurlhttps://github.com/zahraatashgahi/ALACPD.