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.

翻译：变化点检测（CPD）与异常检测（AD）是诸多领域中识别数据突变或异常实例的关键技术。然而，现有方法通常局限于单变量数据，面对大规模数据集时因计算需求而存在可扩展性挑战，且在处理高维或复杂数据以及隐藏异常时性能下降。此外，这些方法往往缺乏可解释性以及对领域特定知识的适应能力，从而限制了其在不同领域的通用性。本文提出一种基于深度学习的CPD/AD方法，称为概率预测编码（PPC），该方法联合学习将序列数据编码为低维潜在空间表示，并预测后续数据表示及其相应的预测不确定性。通过比较预测值与真实编码，采用最大似然估计优化模型参数。在应用阶段，真实编码与预测编码被用于计算符合概率——一种可解释且具有明确意义的异常评分。此外，本方法具有线性时间复杂度，避免了可扩展性问题，并能轻松适配多种数据类型及复杂应用场景。我们通过合成时间序列实验、图像数据以及真实世界的磁共振波谱成像数据，验证了所提方法的有效性与适应性。