  Change Point Detection (CPD) methods identify the times associated with
changes in the trends and properties of time series data in order to describe
the underlying behaviour of the system. For instance, detecting the changes and
anomalies associated with web service usage, application usage or human
behaviour can provide valuable insights for downstream modelling tasks. We
propose a novel approach for self-supervised Time Series Change Point detection
method based onContrastivePredictive coding (TS-CP^2). TS-CP^2 is the first
approach to employ a contrastive learning strategy for CPD by learning an
embedded representation that separates pairs of embeddings of time adjacent
intervals from pairs of interval embeddings separated across time. Through
extensive experiments on three diverse, widely used time series datasets, we
demonstrate that our method outperforms five state-of-the-art CPD methods,
which include unsupervised and semi-supervisedapproaches. TS-CP^2 is shown to
improve the performance of methods that use either handcrafted statistical or
temporal features by 79.4% and deep learning-based methods by 17.0% with
respect to the F1-score averaged across the three datasets.
