Change Point Detection in Multi-Channel Time Series via a Time-Invariant Representation
Zhenxiang Cao, Nick Seeuws, Maarten De Vos, Alexander Bertrand · IEEE Transactions on Knowledge and Data Engineering · 2023
Change Point Detection (CPD) refers to the task of identifying abrupt changes in the characteristics or statistics of time series data. Recent advancements have led to a shift away from traditional model-based CPD approaches, which rely on predefined statistical distributions, toward neural network-based and distribution-free methods using autoencoders. However, many state-of-the-art methods in this category often neglect to explicitly leverage spatial information across multiple channels, making them less effective at detecting changes in cross-channel statistics. In this paper, we introduce an unsupervised, distribution-free CPD method that explicitly incorporates both temporal and spatial (cross-channel) information in multi-channel time series data based on the so-called Time-Invariant Representation (TIRE) autoencoder. Our evaluation, conducted on both simulated and real-life datasets, illustrates the significant advantages of our proposed multi-channel TIRE (MC-TIRE) method, which consistently delivers more accurate CPD results.