  Change point detection (CPD) and anomaly detection (AD) are essential
techniques in various fields to identify abrupt changes or abnormal data
instances. However, existing methods are often constrained to univariate data,
face scalability challenges with large datasets due to computational demands,
and experience reduced performance with high-dimensional or intricate data, as
well as hidden anomalies. Furthermore, they often lack interpretability and
adaptability to domain-specific knowledge, which limits their versatility
across different fields. In this work, we propose a deep learning-based CPD/AD
method called Probabilistic Predictive Coding (PPC) that jointly learns to
encode sequential data to low dimensional latent space representations and to
predict the subsequent data representations as well as the corresponding
prediction uncertainties. The model parameters are optimized with maximum
likelihood estimation by comparing these predictions with the true encodings.
At the time of application, the true and predicted encodings are used to
determine the probability of conformity, an interpretable and meaningful
anomaly score. Furthermore, our approach has linear time complexity,
scalability issues are prevented, and the method can easily be adjusted to a
wide range of data types and intricate applications. We demonstrate the
effectiveness and adaptability of our proposed method across synthetic time
series experiments, image data, and real-world magnetic resonance spectroscopic
imaging data.
