A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
University of California San Diego · NEC (United States)
Abstract
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…
Citation impact
- FWCI
- 61.30
- Percentile
- 100%
- References
- 42
Authors
6Topics & keywords
- Nonlinear autoregressive exogenous model
- Computer science
- Recurrent neural network
- Autoregressive model
- Time series
- Series (stratigraphy)
- Dual (grammatical number)
- Artificial intelligence