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- Tung Kieu
- Department of Computer Science, Aalborg University, Denmark
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- Bin Yang
- Department of Computer Science, Aalborg University, Denmark
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- Chenjuan Guo
- Department of Computer Science, Aalborg University, Denmark
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- Christian S. Jensen
- Department of Computer Science, Aalborg University, Denmark
書誌事項
- 公開日
- 2019-08
- DOI
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- 10.24963/ijcai.2019/378
- 公開者
- International Joint Conferences on Artificial Intelligence Organization
説明
<jats:p>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 large real-world time series data sets, including univariate and multivariate time series, offer insight into the design properties of the proposed frameworks and demonstrate that the resulting solutions are capable of outperforming both baselines and the state-of-the-art methods.</jats:p>
収録刊行物
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- Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
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Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence 2725-2732, 2019-08
International Joint Conferences on Artificial Intelligence Organization
