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 Github\footnote{\url{https://github.com/zahraatashgahi/ALACPD}}.

翻译：变点检测（CPD）用于检测数据分布中的突变，被公认为时间序列分析中最关键的任务之一。尽管离线CPD已有大量文献，但无监督在线CPD仍面临重大挑战，包括可扩展性、超参数调优和学习约束。为缓解部分挑战，本文提出一种用于多维时间序列无监督在线CPD的新型深度学习方法——自适应LSTM自编码器变点检测（ALACPD）。ALACPD利用基于LSTM自编码器的神经网络执行无监督在线CPD，通过持续适应新样本且无需保留先前输入实现无记忆性。我们在多个真实世界时间序列CPD基准数据集上进行广泛评估。结果表明，ALACPD在时间序列分割质量方面平均排名第一，且在变点估计精度上与最佳方法持平。ALACPD的实现代码已在GitHub上公开（\url{https://github.com/zahraatashgahi/ALACPD}）。