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A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data

Published: 27 January 2019 Publication History

Abstract

Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in multi-variate time series refer to identifying abnormal status in certain time steps and pinpointing the root causes. Building such a system, however, is challenging since it not only requires to capture the temporal dependency in each time series, but also need encode the inter-correlations between different pairs of time series. In addition, the system should be robust to noise and provide operators with different levels of anomaly scores based upon the severity of different incidents. Despite the fact that a number of unsupervised anomaly detection algorithms have been developed, few of them can jointly address these challenges. In this paper, we propose a Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform anomaly detection and diagnosis in multivariate time series data. Specifically, MSCRED first constructs multi-scale (resolution) signature matrices to characterize multiple levels of the system statuses in different time steps. Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns. Finally, based upon the feature maps which encode the inter-sensor correlations and temporal information, a convolutional decoder is used to reconstruct the input signature matrices and the residual signature matrices are further utilized to detect and diagnose anomalies. Extensive empirical studies based on a synthetic dataset and a real power plant dataset demonstrate that MSCRED can outperform state-of-the-art baseline methods.

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        cover image Guide Proceedings
        AAAI'19/IAAI'19/EAAI'19: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence
        January 2019
        10088 pages
        ISBN:978-1-57735-809-1

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        • Association for the Advancement of Artificial Intelligence

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        AAAI Press

        Publication History

        Published: 27 January 2019

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        • (2024)SVMPT: A Hybrid Approach to Sparse and Irregular Clinical Data Learning with Selective Variable-wise Message Passing and TransformerProceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3698587.3701350(1-8)Online publication date: 22-Nov-2024
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        • (2024)SA2E-AD: A Stacked Attention Autoencoder for Anomaly Detection in Multivariate Time SeriesACM Transactions on Knowledge Discovery from Data10.1145/365367718:7(1-15)Online publication date: 26-Mar-2024
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