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Time series forecasting is a significant problem in many applications, e.g., financial predictions and business optimization.
Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence , 2019, pp. 1907–1913
1913
Earlier work this paper cites.
E. McKenzie, “General exponential smoothing and the equivalent ARMA process,” Journal of Forecasting , vol. 3, no. 3, pp. 333–344, 1984
1984
Earlier work this paper cites.
F. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: Continual prediction with LSTM,” in Proceedings of the 9th International Conference on Artificial Neural Networks , vol. 2, 1999, pp. 850–855
1999
Earlier work this paper cites.
2004
Earlier work this paper cites.
R. Hyndman, A. Koehler, J. Ord, and R. Snyder, Forecasting with Exponential Smoothing: The State Space Approach , ser. Springer Series in Statistics. Springer Berlin Heidelberg, 2008
2008
Earlier work this paper cites.
B. Gui, X. Wei, Q. Shen, J. Qi, and L. Guo, “Financial time series forecasting using support vector machine,” in Proceedings of the 10th International Conference on Computational Intelligence and Security , 2014, pp. 39–43
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in Proceedings of the 28th Conference on Neural Information Processing Systems , vol. 27, 2014, p. 3104–3112
2014
Earlier work this paper cites.
M. W. Seeger, D. Salinas, and V. Flunkert, “Bayesian intermittent demand forecasting for large inventories,” in Proceedings of the 30th Conference on Neural Information Processing Systems , vol. 29, 2016, p. 4646–4654
2016
Earlier work this paper cites.
H.-F. Yu, N. Rao, and I. S. Dhillon, “Temporal regularized matrix factorization for high-dimensional time series prediction,” in Proceedings of the 30th Conference on Neural Information Processing Systems , vol. 29, 2016, p. 847–855
2016
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proceedings of the 31st Conference on Neural Information Processing Systems , vol. 30, 2017, p. 5998–6008
2017
Cited alongside, same era.
A. van den Oord, O. Vinyals, and koray Kavukcuoglu, “Neural discrete representation learning,” in Proceedings of the 31st Conference on Neural Information Processing Systems , vol. 30, 2017, p. 6306–6315
2017
Cited alongside, same era.
2017
S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y.-X. Wang, and X. Yan, “Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting,” in Proceedings of the 33rd Conference on Neural Information Processing Systems , vol. 32, 2019
2019
Later among the works it cites.
R. Sen, H.-F. Yu, and I. S. Dhillon, “Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting,” in Proceedings of the 33rd Conference on Neural Information Processing Systems , vol. 32, 2019, p. 4838–4847
2019
Later among the works it cites.
L. Bai, L. Yao, C. Li, X. Wang, and C. Wang, “Adaptive graph convolutional recurrent network for traffic forecasting,” in Proceedings of the 34th Conference on Neural Information Processing Systems , vol. 33, 2020, pp. 17 804–17 815
2020
Later among the works it cites.
W. Chen, L. Chen, Y. Xie, W. Cao, Y. Gao, and X. Feng, “Multi-range attentive bicomponent graph convolutional network for traffic forecasting,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence , vol. 34, 2020, pp. 3529–3536
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Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in 6th International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence , 2018, pp. 3634–3640
2018
Cited alongside, same era.
S. S. Rangapuram, M. W. Seeger, J. Gasthaus, L. Stella, Y. Wang, and T. Januschowski, “Deep state space models for time series forecasting,” in Proceedings of the 32nd Conference on Neural Information Processing Systems , vol. 31, 2018, p. 7796–7805
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2020
Later among the works it cites.
Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, and C. Zhang, “Connecting the dots: Multivariate time series forecasting with graph neural networks,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &; Data Mining , 2020, p. 753–763
2020
Later among the works it cites.
H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, “Informer: Beyond efficient transformer for long sequence time-series forecasting,” in Proceedings of the 35th AAAI Conference on Artificial Intelligence , vol. 35, 2021, pp. 11 106–11 115
2021
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M. Lv, Z. Hong, L. Chen, T. Chen, T. Zhu, and S. Ji, “Temporal multi-graph convolutional network for traffic flow prediction,” IEEE Transactions on Intelligent Transportation Systems , vol. 22, no. 6, pp. 3337–3348, 2021
2021
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2021
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