Robust Online Time Series Prediction with Recurrent Neural Networks

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.

Published in:
Proceedings Of 3Rd Ieee/Acm International Conference On Data Science And Advanced Analytics, (Dsaa 2016), 816-825
Presented at:
3rd IEEE/ACM International Conference on Data Science and Advanced Analytics (DSAA), Montral, CANADA, OCT 17-19, 2016
New York, Ieee

 Record created 2017-02-17, last modified 2018-01-28

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