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Spatio-temporal forecasting is an open research field whose interest is growing exponentially.
1906
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1909
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1909
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2014
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K. Cho, B. van Merriënboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder–decoder approaches,” in Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation . Doha, Qatar: Association for Computational Linguistics, Oct. 2014, pp. 103–111
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A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social LSTM: Human Trajectory Prediction in Crowded Spaces,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Jun. 2016, pp. 961–971, iSSN: 1063-6919
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2016
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2016
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2016
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Q. You, H. Jin, Z. Wang, C. Fang, and J. Luo, “Image captioning with semantic attention,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2016
2016
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Y. Wang, M. Long, J. Wang, Z. Gao, and P. S. Yu, “PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs,” in Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 879–888
2017
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H.-F. Yang, T. S. Dillon, and Y.-P. P. Chen, “Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach,” IEEE Transactions on Neural Networks and Learning Systems , vol. 28, no. 10, pp. 2371–2381, Oct. 2017, conference Name: IEEE Transactions on Neural Networks and Learning Systems
E. M. de Oliveira and F. L. Cyrino Oliveira, “Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods,” Energy , vol. 144, pp. 776–788, Feb. 2018
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Q. Tang, M. Yang, and Y. Yang, “ST-LSTM: A Deep Learning Approach Combined Spatio-Temporal Features for Short-Term Forecast in Rail Transit,” 2019, iSSN: 0197-6729 Library Catalog: www.hindawi.com Pages: e8392592 Publisher: Hindawi Volume: 2019
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2017
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2017
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M. Sundararajan, A. Taly, and Q. Yan, “Axiomatic attribution for deep networks,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 3319–3328
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S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in neural information processing systems , 2017, pp. 4765–4774
2017
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D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” ICLR 2015 , Jan. 2017, arXiv: 1412.6980
2017
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Q. Zhu, J. Chen, L. Zhu, X. Duan, and Y. Liu, “Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach,” Energies , vol. 11, no. 4, p. 705, Apr. 2018, number: 4 Publisher: Multidisciplinary Digital Publishing Institute
2018
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B. Liao, F. Wu, J. Zhang, M. Cai, S. Tang, Y. Gao, C. Wu, S. Yang, W. Zhu, and Y. Guo, “Dest-ResNet: A Deep Spatiotemporal Residual Network for Hotspot Traffic Speed Prediction,” in 2018 ACM Multimedia Conference on Multimedia Conference - MM ’18 . Seoul, Republic of Korea: ACM Press, 2018, pp. 1883–1891
2018
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B. Liao, J. Zhang, C. Wu, D. McIlwraith, T. Chen, S. Yang, Y. Guo, and F. Wu, “Deep Sequence Learning with Auxiliary Information for Traffic Prediction,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , ser. KDD ’18. London, United Kingdom: Association for Computing Machinery, Jul. 2018, pp. 537–546
2018
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W. Cheng, Y. Shen, Y. Zhu, and L. Huang, “A neural attention model for urban air quality inference: Learning the weights of monitoring stations,” in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 , S. A. McIlraith and K. Q. Weinberger, Eds. AAAI Press, 2018, pp. 2151–2158
2018
Cited alongside, same era.
J. Ke, H. Yang, H. Zheng, X. Chen, Y. Jia, P. Gong, and J. Ye, “Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services,” IEEE Transactions on Intelligent Transportation Systems , vol. 20, no. 11, pp. 4160–4173, Nov. 2019, conference Name: IEEE Transactions on Intelligent Transportation Systems
2019
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L. N. N. Do, H. L. Vu, B. Q. Vo, Z. Liu, and D. Phung, “An effective spatial-temporal attention based neural network for traffic flow prediction,” Transportation Research Part C: Emerging Technologies , vol. 108, pp. 12–28, Nov. 2019
2019
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S. Guo, Y. Lin, S. Li, Z. Chen, and H. Wan, “Deep Spatial–Temporal 3d Convolutional Neural Networks for Traffic Data Forecasting,” IEEE Transactions on Intelligent Transportation Systems , vol. 20, no. 10, pp. 3913–3926, Oct. 2019
2019
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S. Deng, S. Jia, and J. Chen, “Exploring spatial–temporal relations via deep convolutional neural networks for traffic flow prediction with incomplete data,” Applied Soft Computing , vol. 78, pp. 712–721, May 2019
2019
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L. Qu, W. Li, W. Li, D. Ma, and Y. Wang, “Daily long-term traffic flow forecasting based on a deep neural network,” Expert Systems with Applications , vol. 121, pp. 304–312, May 2019
2019
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Z. He, C.-Y. Chow, and J.-D. Zhang, “STCNN: A Spatio-Temporal Convolutional Neural Network for Long-Term Traffic Prediction,” in 2019 20th IEEE International Conference on Mobile Data Management (MDM) , Jun. 2019, pp. 226–233, iSSN: 1551-6245
2019
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X. Dai, R. Fu, E. Zhao, Z. Zhang, Y. Lin, F.-Y. Wang, and L. Li, “DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending,” Transportation Research Part C: Emerging Technologies , vol. 103, pp. 142–157, Jun. 2019
2019
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J. Wang, R. Chen, and Z. He, “Traffic speed prediction for urban transportation network: A path based deep learning approach,” Transportation Research Part C: Emerging Technologies , vol. 100, pp. 372 – 385, 2019
2019
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Z. Cui, K. Henrickson, R. Ke, X. Dong, and Y. Wang, “High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting,” 2019, number: 19-05236
2019
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J. Wu, X.-Y. Chen, H. Zhang, L.-D. Xiong, H. Lei, and S.-H. Deng, “Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimizationb,” Journal of Electronic Science and Technology , vol. 17, no. 1, pp. 26–40, Mar. 2019
2019
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C. K. Wikle, A. Zammit-Mangion, and N. Cressie, Spatio-Temporal Statistics with R , 1st ed. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, Feb. 2019. [Online]. Available: https://www.taylorfrancis.com/books/9780429649783
2019
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Y. Chen, G. Peng, Z. Zhu, and S. Li, “A novel deep learning method based on attention mechanism for bearing remaining useful life prediction,” Applied Soft Computing , vol. 86, p. 105919, Jan. 2020
2020
Closest in time.
T. Bogaerts, A. D. Masegosa, J. S. Angarita-Zapata, E. Onieva, and P. Hellinckx, “A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data,” Transportation Research Part C: Emerging Technologies , vol. 112, pp. 62–77, Mar. 2020
2020
Closest in time.