Deep learning for multivariate time series anomaly detection: an evaluation of reconstruction-based methods

Abstract In the field of anomaly detection in time series, remarkable advances based on deep learning methodologies and, more specifically, reconstruction-based methods have been proposed. These methods are particularly valuable, as they can capture the fundamental structure of the data and enable the detection of subtle anomalies that traditional techniques might overlook. Reviews of the existing literature discuss anomaly detection from a general perspective and consider a specific anomaly type, hindering the process of proposing new and better algorithms. This paper focuses on reconstruction-based methods in isolation, as they have been demonstrated to present the best performance of the three main groups in deep anomaly detection models described so far. Thus, it intends to extend the literature by addressing in detail reconstruction-based methods for anomaly detection in multivariate time series, to provide richer information about these methods, and to include extensive experimentation, not usually performed in existing surveys on the topic. Finally, the paper presents useful insights (strengths and weaknesses of the methods) extracted from the experimental study, and significant challenges and future research directions related to the potential of these methods in anomaly detection.