Name
Abstract | ||
|---|---|---|
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.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3269206.3269310 | CIKM |
Keywords | Field | DocType |
Correlated time series, Deep learning, Multi-Task Learning | Data mining,Time series,Digitization,Multi-task learning,Computer science,Convolutional neural network,Recurrent neural network,Artificial intelligence,Deep learning,Deep neural networks,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6014-2 | 7 | 0.44 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Razvan-Gabriel Cirstea | 1 | 7 | 0.78 |
| Darius-Valer Micu | 2 | 7 | 0.44 |
| Gabriel-Marcel Muresan | 3 | 7 | 0.44 |
| Chenjuan Guo | 4 | 301 | 16.81 |
| Bin Yang | 5 | 706 | 34.93 |