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Change-point detection (CPD), which detects abrupt changes in the datadistribution, is recognized as one of the most significant tasks in time seriesanalysis. Despite the extensive literature on offline CPD, unsupervised onlineCPD still suffers from major challenges, including scalability, hyperparametertuning, and learning constraints. To mitigate some of these challenges, in thispaper, we propose a novel deep learning approach for unsupervised online CPDfrom multi-dimensional time series, named Adaptive LSTM-AutoencoderChange-Point Detection (ALACPD). ALACPD exploits an LSTM-autoencoder-basedneural network to perform unsupervised online CPD. It continuously adapts tothe incoming samples without keeping the previously received input, thus beingmemory-free. We perform an extensive evaluation on several real-world timeseries CPD benchmarks. We show that ALACPD, on average, ranks first amongstate-of-the-art CPD algorithms in terms of quality of the time seriessegmentation, and it is on par with the best performer in terms of the accuracyof the estimated change-points. The implementation of ALACPD is availableonline on Github\footnote{\url{https://github.com/zahraatashgahi/ALACPD}}.

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