Anomalous Change Point Detection Using Probabilistic Predictive Coding

TL;DR

This paper introduces Probabilistic Predictive Coding, a deep learning approach for change point and anomaly detection that is scalable, interpretable, and adaptable to various data types, outperforming existing methods especially in high-dimensional settings.

Contribution

It presents a novel deep learning-based method that encodes data into low-dimensional representations and predicts future encodings with uncertainties, enabling scalable and interpretable anomaly detection.

Findings

Effective on synthetic time series data

Demonstrates adaptability to image and medical data

Achieves linear time complexity

Abstract

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 conformance, 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.