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Article

Outlier detection for time series with recurrent autoencoder ensembles

Authors:

Christian S. JensenAuthors Info & Claims

IJCAI'19: Proceedings of the 28th International Joint Conference on Artificial Intelligence

Pages 2725 - 2732

Published: 10 August 2019 Publication History

Abstract

We propose two solutions to outlier detection in time series based on recurrent autoencoder ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent neural networks (S-RNNs). Such networks make it possible to generate multiple autoencoders with different neural network connection structures. The two solutions are ensemble frameworks, specifically an independent framework and a shared framework, both of which combine multiple S-RNN based autoencoders to enable outlier detection. This ensemble-based approach aims to reduce the effects of some autoencoders being overfitted to outliers, this way improving overall detection quality. Experiments with two real-world time series data sets, including univariate and multivariate time series, offer insight into the design properties of the proposed ensemble frameworks and demonstrate that the proposed frameworks are capable of outperforming both baselines and the state-of-the-art methods.

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Cited By

Wang RMou XWo TZhang MLiu YWang TLiu PYan JLiu X(2025)ACbot: an IIoT platform for industrial robotsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-3449-x19:4Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s11704-024-3449-x
Campos DKieu TGuo CHuang FZheng KYang BJensen C(2022)Unsupervised time series outlier detection with diversity-driven convolutional ensemblesProceedings of the VLDB Endowment10.14778/3494124.349414215:3(611-623)Online publication date: 4-Feb-2022
https://dl.acm.org/doi/10.14778/3494124.3494142

Outlier detection for time series with recurrent autoencoder ensembles
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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Published In

cover image Guide Proceedings

IJCAI'19: Proceedings of the 28th International Joint Conference on Artificial Intelligence

August 2019

6589 pages

ISBN:9780999241141

Editor:
Sarit Kraus
Bar-Ilan University (ISRAEL)

Sponsors

Sony: Sony Corporation
Huawei Technologies Co. Ltd.: Huawei Technologies Co. Ltd.
Baidu Research: Baidu Research
The International Joint Conferences on Artificial Intelligence, Inc. (IJCAI)
Lenovo: Lenovo

Publisher

AAAI Press

Publication History

Published: 10 August 2019

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Cited By

Wang RMou XWo TZhang MLiu YWang TLiu PYan JLiu X(2025)ACbot: an IIoT platform for industrial robotsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-3449-x19:4Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s11704-024-3449-x
Campos DKieu TGuo CHuang FZheng KYang BJensen C(2022)Unsupervised time series outlier detection with diversity-driven convolutional ensemblesProceedings of the VLDB Endowment10.14778/3494124.349414215:3(611-623)Online publication date: 4-Feb-2022
https://dl.acm.org/doi/10.14778/3494124.3494142

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