Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval

  • Dixian Zhu University of Iowa
  • Dongjin Song NEC Laboratories America, Inc.
  • Yuncong Chen NEC Laboratories America, Inc.
  • Cristian Lumezanu NEC Laboratories America, Inc.
  • Wei Cheng NEC Laboratories America, Inc.
  • Bo Zong NEC Laboratories America, Inc.
  • Jingchao Ni NEC Laboratories America, Inc.
  • Takehiko Mizoguchi NEC Laboratories America, Inc.
  • Tianbao Yang University of Iowa
  • Haifeng Chen NEC Laboratories America, Inc.


Multivariate time series data are becoming increasingly ubiquitous in varies real-world applications such as smart city, power plant monitoring, wearable devices, etc. Given the current time series segment, how to retrieve similar segments within the historical data in an efficient and effective manner is becoming increasingly important. As it can facilitate underlying applications such as system status identification, anomaly detection, etc. Despite the fact that various binary coding techniques can be applied to this task, few of them are specially designed for multivariate time series data in an unsupervised setting. To this end, we present Deep Unsupervised Binary Coding Networks (DUBCNs) to perform multivariate time series retrieval. DUBCNs employ the Long Short-Term Memory (LSTM) encoder-decoder framework to capture the temporal dynamics within the input segment and consist of three key components, i.e., a temporal encoding mechanism to capture the temporal order of different segments within a mini-batch, a clustering loss on the hidden feature space to capture the hidden feature structure, and an adversarial loss based upon Generative Adversarial Networks (GANs) to enhance the generalization capability of the generated binary codes. Thoroughly empirical studies on three public datasets demonstrated that the proposed DUBCNs can outperform state-of-the-art unsupervised binary coding techniques.

AAAI Technical Track: Computational Sustainability