The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
Open accessAbstract
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.
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Cited by
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17325 · Cited by 7,752
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Deng, A., & Hooi, B. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i5.16523 · Cited by 1,413
Recurrent Neural Networks for Time Series Forecasting: Current status and future directions
Hewamalage, H., Bergmeir, C., & Bandara, K. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.06.008 · Cited by 1,371
Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Zong, B., Chen, H., & Chawla, N. V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011409 · Cited by 855
Self-citationDynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
Li, F., Feng, J., Yan, H., Jin, G., Yang, F., Sun, F., Jin, D., & Li, Y. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3532611 · Cited by 555
Metadata
- Authors
- Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., & Cottrell, G. W.
- Venue
- Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
- Published
- 2017-08
- Pages
- 2627-2633
- OpenAlex
- W2613328025
- Cited by
- 1,585
- References
- 7
- FWCI
- 111.73
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., & Cottrell, G. W. (2017). A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2017/366
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