Outlier Detection for Time Series with Recurrent Autoencoder Ensembles

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

21 Citations (Scopus)

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 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.
Original languageEnglish
Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
EditorsSarit Kraus
Number of pages8
Publisherijcai.org
Publication date2019
Pages2725-2732
ISBN (Electronic)9780999241141
DOIs
Publication statusPublished - 2019
Eventthe 28th International Joint Conference on Artificial Intelligence - Macau, China
Duration: 10 Aug 2019 → …
https://www.ijcai19.org/

Conference

Conferencethe 28th International Joint Conference on Artificial Intelligence
CountryChina
CityMacau
Period10/08/2019 → …
Internet address

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