Abstract
Time series forecasting for streaming data plays an important role in many real applications, ranging from IoT systems, cyber-networks, to industrial systems and healthcare. However the real data is often complicated with anomalies and change points, which can lead the learned models deviating from the underlying patterns of the time series, especially in the context of online learning mode. In this paper we present an adaptive gradient learning method for recurrent neural networks (RNN) to forecast streaming time series in the presence of anomalies and change points. We explore the local features of time series to automatically weight the gradients of the loss of the newly available observations with distributional properties of the data in real time. We perform extensive experimental analysis on both synthetic and real datasets to evaluate the performance of the proposed method.
Original language | English |
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Title of host publication | Proceedings: 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016 |
Publisher | IEEE |
Pages | 816-825 |
Number of pages | 10 |
ISBN (Electronic) | 9781509052066 |
DOIs | |
Publication status | Published - 22 Dec 2016 |
Externally published | Yes |
Event | 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016 - Montreal, Canada Duration: 17 Oct 2016 → 19 Oct 2016 |
Publication series
Name | International Conference on Data Science and Advanced Analytics (DSAA) |
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Conference
Conference | 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016 |
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Country/Territory | Canada |
City | Montreal |
Period | 17/10/16 → 19/10/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.