Correlated Time Series Forecasting using Multi-Task Deep Neural Networks

Razvan-Gabriel Cirstea, Darius-Valer Micu, Gabriel-Marcel Muresan, Chenjuan Guo, Bin Yang

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

44 Citationer (Scopus)

Abstrakt

Cyber-physical systems often consist of entities that interact with each other over time. Meanwhile, as part of the continued digitization of industrial processes, various sensor technologies are deployed that enable us to record time-varying attributes (a.k.a., time series) of such entities, thus producing correlated time series. To enable accurate forecasting on such correlated time series, this paper proposes two models that combine convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first model employs a CNN on each individual time series, combines the convoluted features, and then applies an RNN on top of the convoluted features in the end to enable forecasting. The second model adds additional auto-encoders into the individual CNNs, making the second model a multi-task learning model, which provides accurate and robust forecasting. Experiments on a large real-world correlated time series data set suggest that the proposed two models are effective and outperform baselines in most settings.
OriginalsprogEngelsk
TitelCIKM '18 Proceedings of the 27th ACM International Conference on Information and Knowledge Management
RedaktørerNorman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
Antal sider4
ForlagAssociation for Computing Machinery
Publikationsdato17 okt. 2018
Sider1527-1530
ISBN (Elektronisk)978-1-4503-6014-2
DOI
StatusUdgivet - 17 okt. 2018
Begivenhed27th ACM International Conference on Information and Knowledge Management - Torino, Italien
Varighed: 22 okt. 201826 okt. 2018
http://www.cikm2018.units.it/

Konference

Konference27th ACM International Conference on Information and Knowledge Management
Land/OmrådeItalien
ByTorino
Periode22/10/201826/10/2018
Internetadresse

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