Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals

By Yu-xin Zhang, Yiqiang Chen, Jindong Wang, Zhiwen Pan. IEEE Transactions on Knowledge and Data Engineering, 2021

342 citations recorded by the source corpus.

Nowadays, multi-sensor technologies are applied in many fields, e.g., Health Care (HC), Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can generate a substantial amount of multivariate time-series data. Unsupervised anomaly detection on multi-sensor time-series data has been proven critical in machine learning researches. The key challenge is to discover generalized normal patterns by capturing spatial-temporal correlation in multi-sensor data. Beyond this challenge, the noisy data is often intertwined with the training data, which is likely to mislead the model by making it hard to distinguish between the normal, abnormal, and noisy data. Few of previous researches can jointly address these two challenges. In this paper, we propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M). We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) to better distinguish between the noisy, normal, and abnormal data. Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bidirectional LSTM with Attention) to capture temporal dependence from time-series data. Finally, CAE-M jointly optimizes these two subnetworks. We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on HAR and HC datasets. Experimental results demonstrate that our proposed model outperforms these existing methods.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c0e2b7073c1234aa401a57b7fe10c44a87549d5c

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Citations in the corpus

Deep Learning for Time Series Anomaly Detection: A Survey (2022, neutral): “By capturing spatiotemporal correlation in multisensor time series, the CAE-M (Deep Convolutional Autoencoding Memory network) [193] can model generalised patterns based on normalised data by undertaking reconstruction and prediction simultaneously.”
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (2022, methodology): “The OmniAnomaly, CAE-M and MSCRED models use sequential observations as input, allowing these methods to retain the temporal information.”