Recurrent auto-encoder with multi-resolution ensemble and predictive coding for multivariate time-series anomaly detection

Heejeong Choi, Subin Kim, Pilsung Kang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


As large-scale time-series data can easily be found in real-world applications, multivariate time-series anomaly detection has played an essential role in diverse industries. It enables productivity improvement and maintenance cost reduction by preventing malfunctions and detecting anomalies based on time-series data. However, multivariate time-series anomaly detection is challenging because real-world time-series data exhibit complex temporal dependencies. For this task, it is crucial to learn a rich representation that effectively contains the nonlinear temporal dynamics of normal behavior. In this study, we propose an unsupervised multivariate time-series anomaly detection model named RAE-MEPC which learns informative normal representations based on multi-resolution ensemble reconstruction and predictive coding. We introduce multi-resolution ensemble encoding to capture the multi-scale dependency from the input time series. The encoder hierarchically aggregates the multi-scale temporal features extracted from the sub-encoders with different encoding lengths. From these encoded features, the reconstruction decoder reconstructs the input time series based on multi-resolution ensemble decoding where lower-resolution information helps to decode sub-decoders with higher-resolution outputs. Predictive coding is further introduced to encourage the model to learn more temporal dependencies of the time series. Experiments on real-world benchmark datasets show that the proposed model outperforms the benchmark models for multivariate time-series anomaly detection.

Original languageEnglish
Pages (from-to)25330-25342
Number of pages13
JournalApplied Intelligence
Issue number21
Publication statusPublished - 2023 Nov

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.


  • Multi-resolution ensemble
  • Predictive coding
  • Recurrent auto-encoder
  • Time-series anomaly detection

ASJC Scopus subject areas

  • Artificial Intelligence


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