2,781 research outputs found
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
The Nonlinear autoregressive exogenous (NARX) model, which predicts the
current value of a time series based upon its previous values as well as the
current and past values of multiple driving (exogenous) series, has been
studied for decades. Despite the fact that various NARX models have been
developed, few of them can capture the long-term temporal dependencies
appropriately and select the relevant driving series to make predictions. In
this paper, we propose a dual-stage attention-based recurrent neural network
(DA-RNN) to address these two issues. In the first stage, we introduce an input
attention mechanism to adaptively extract relevant driving series (a.k.a.,
input features) at each time step by referring to the previous encoder hidden
state. In the second stage, we use a temporal attention mechanism to select
relevant encoder hidden states across all time steps. With this dual-stage
attention scheme, our model can not only make predictions effectively, but can
also be easily interpreted. Thorough empirical studies based upon the SML 2010
dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can
outperform state-of-the-art methods for time series prediction.Comment: International Joint Conference on Artificial Intelligence (IJCAI),
201
Neural Networks for Complex Data
Artificial neural networks are simple and efficient machine learning tools.
Defined originally in the traditional setting of simple vector data, neural
network models have evolved to address more and more difficulties of complex
real world problems, ranging from time evolving data to sophisticated data
structures such as graphs and functions. This paper summarizes advances on
those themes from the last decade, with a focus on results obtained by members
of the SAMM team of Universit\'e Paris
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