Outlier Detection for Time Series with Recurrent Autoencoder Ensembles

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13 Citationer (Scopus)

Abstrakt

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.
OriginalsprogEngelsk
TitelProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
RedaktørerSarit Kraus
Antal sider8
Forlagijcai.org
Publikationsdato2019
Sider2725-2732
ISBN (Elektronisk)9780999241141
DOI
StatusUdgivet - 2019
Begivenhedthe 28th International Joint Conference on Artificial Intelligence - Macau, Kina
Varighed: 10 aug. 2019 → …
https://www.ijcai19.org/

Konference

Konferencethe 28th International Joint Conference on Artificial Intelligence
LandKina
ByMacau
Periode10/08/2019 → …
Internetadresse

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  • Citationsformater

    Kieu, T., Yang, B., Guo, C., & Jensen, C. S. (2019). Outlier Detection for Time Series with Recurrent Autoencoder Ensembles. I S. Kraus (red.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (s. 2725-2732). ijcai.org. https://doi.org/10.24963/ijcai.2019/378