A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data


  • Chuxu Zhang University of Notre Dame
  • Dongjin Song NEC Labs America
  • Yuncong Chen NEC Laboratories America, Inc.
  • Xinyang Feng Columbia University
  • Cristian Lumezanu NEC Labs
  • Wei Cheng NEC Laboratories America
  • Jingchao Ni NEC Laboratories America, Inc.
  • Bo Zong NEC Labs
  • Haifeng Chen NEC Labs
  • Nitesh V. Chawla University of Notre Dame




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 multivariate 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-ofthe-art baseline methods.




How to Cite

Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H., & Chawla, N. V. (2019). A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 1409-1416. https://doi.org/10.1609/aaai.v33i01.33011409



AAAI Technical Track: Computational Sustainability