Conference Paper2025
SSMT: Few-Shot Traffic Forecasting with Single Source Meta-transfer
Kishor Kumar Bhaumik, Minha Kim, Fahim Faisal Niloy, Amin Ahsan Ali, Simon S. Woo
Pattern Recognition
Springer Nature Switzerland, pp. 46-61, ISBN: 9783031781940
Keywords
Computer scienceShot (pellet)Transfer (computing)Single shotArtificial intelligence
References
- 1.Bing Yu, Haoteng Yin, Zhanxing Zhu. (2018). Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. , 3634–3640[10.24963/ijcai.2018/505]
- 2.Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, Haifeng Li. (2019). T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems, 21(9), 3848–3858[10.1109/tits.2019.2935152]
- 3.Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang. (2019). Graph WaveNet for Deep Spatial-Temporal Graph Modeling. , 1907–1913[10.24963/ijcai.2019/264]
- 4.Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, Anton Van Den Hengel. (2019). Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection. , 1705–1714[10.1109/iccv.2019.00179]
- 5.Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap. (2016). Meta-learning with memory-augmented neural networks. International Conference on Machine Learning, 1842–1850
- 6.Hyunjong Park, Jongyoun Noh, Bumsub Ham. (2020). Learning Memory-Guided Normality for Anomaly Detection. , 14360–14369[10.1109/cvpr42600.2020.01438]
- 7.Mohammed S. Ahmed, A R Cook. (1979). ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES. Transportation Research Record Journal of the Transportation Research Board(722)
- 8.Shengnan Guo, Youfang Lin, Huaiyu Wan, Xiucheng Li, Gao Cong. (2021). Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting. IEEE Transactions on Knowledge and Data Engineering, 34(11), 5415–5428[10.1109/tkde.2021.3056502]
- 9.Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, Junbo Zhang. (2019). Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning. , 1720–1730[10.1145/3292500.3330884]
- 10.Wen Zhang, Lingfei Deng, Lei Zhang, Dongrui Wu. (2022). A Survey on Negative Transfer. IEEE/CAA Journal of Automatica Sinica, 10(2), 305–329[10.1109/jas.2022.106004]
- 11.Lei Bai, Lina Yao, Salil S. Kanhere, Xianzhi Wang, Quan Z. Sheng. (2019). STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting. , 1981–1987[10.24963/ijcai.2019/274]
- 12.Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, Toyotaro Suzumura. (2023). Spatio-Temporal Meta-Graph Learning for Traffic Forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 37(7), 8078–8086[10.1609/aaai.v37i7.25976]
- 13.Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, Zhenhui Li. (2019). Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction. , 2181–2191[10.1145/3308558.3313577]
- 14.Yuntao Du, Jindong Wang, Wenjie Feng, Sinno Pan, Tao Qin, Renjun Xu, Chongjun Wang. (2021). AdaRNN. , 402–411[10.1145/3459637.3482315]
- 15.Leye Wang, Xu Geng, Xiaojuan Ma, Feng Liu, Qiang Yang. (2019). Cross-City Transfer Learning for Deep Spatio-Temporal Prediction. , 1893–1899[10.24963/ijcai.2019/262]
- 16.Bin Lu, Xiaoying Gan, Haiming Jin, Luoyi Fu, Haisong Zhang. (2020). Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting. , 1025–1034[10.1145/3340531.3411894]
- 17.Bin Lu, Xiaoying Gan, Weinan Zhang, Huaxiu Yao, Luoyi Fu, Xinbing Wang. (2022). Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1162–1172[10.1145/3534678.3539281]
- 18.Ankith Jain Rakesh Kumar, Bir Bhanu. (2021). Micro-Expression Classification based on Landmark Relations with Graph Attention Convolutional Network. , 1511–1520[10.1109/cvprw53098.2021.00167]
- 19.Yilun Jin, Kai Chen, Qiang Yang. (2022). Selective Cross-City Transfer Learning for Traffic Prediction via Source City Region Re-Weighting. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 731–741[10.1145/3534678.3539250]
- 20.Hongyuan Yu, Ting Li, Weichen Yu, Jianguo Li, Yan Huang, Liang Wang, Alex Liu. (2022). Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2362–2368[10.24963/ijcai.2022/328]
- 21.Zhanyu Liu, Guanjie Zheng, Yanwei Yu. (2023). Cross-city Few-Shot Traffic Forecasting via Traffic Pattern Bank. , 1451–1460[10.1145/3583780.3614829]
- 22.Tien Huu Do, Evaggelia Tsiligianni, Xuening Qin, Jelle Hofman, Valerio Panzica La Manna, Wilfried Philips, Nikos Deligiannis. (2020). Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors. IEEE Internet of Things Journal, 7(9), 8943–8955[10.1109/jiot.2020.2999446]
- 23.Yingxue Zhang, Yanhua Li, Xun Zhou, Xiangnan Kong, Jun Luo. (2022). STrans-GAN: Spatially-Transferable Generative Adversarial Networks for Urban Traffic Estimation. 2022 IEEE International Conference on Data Mining (ICDM), 743–752[10.1109/icdm54844.2022.00085]
- 24.Finn, Chelsea, Abbeel, Pieter, Levine, Sergey. (2017). Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. arXiv (Cornell University)[10.48550/arxiv.1703.03400]
- 25.Cini, Andrea, Marisca, Ivan, Zambon, Daniele, Alippi, Cesare. (2023). Taming Local Effects in Graph-based Spatiotemporal Forecasting. arXiv (Cornell University)[10.48550/arxiv.2302.04071]