TY - GEN
T1 - Correlated Time Series Forecasting using Multi-Task Deep Neural Networks
AU - Cirstea, Razvan-Gabriel
AU - Micu, Darius-Valer
AU - Muresan, Gabriel-Marcel
AU - Guo, Chenjuan
AU - Yang, Bin
PY - 2018/10/17
Y1 - 2018/10/17
N2 - 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.
AB - 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.
KW - Correlated time series
KW - Deep learning
KW - Multi-Task Learning
UR - http://www.scopus.com/inward/record.url?scp=85058051566&partnerID=8YFLogxK
U2 - 10.1145/3269206.3269310
DO - 10.1145/3269206.3269310
M3 - Article in proceeding
SP - 1527
EP - 1530
BT - CIKM '18 Proceedings of the 27th ACM International Conference on Information and Knowledge Management
A2 - Paton, Norman
A2 - Candan, Selcuk
A2 - Wang, Haixun
A2 - Allan, James
A2 - Agrawal, Rakesh
A2 - Labrinidis, Alexandros
A2 - Cuzzocrea, Alfredo
A2 - Zaki, Mohammed
A2 - Srivastava, Divesh
A2 - Broder, Andrei
A2 - Schuster, Assaf
PB - Association for Computing Machinery (ACM)
T2 - 27th ACM International Conference on Information and Knowledge Management
Y2 - 22 October 2018 through 26 October 2018
ER -