{"type":"ScholarlyArticle","title":"Reconstructing Missing Variables for Multivariate Time Series Forecasting via Conditional Generative Flows","authors":[{"name":"Xuanming Hu","slug":"xuanming-hu","given":"Xuanming","family":"Hu","affiliation":"School of Computing and Augmented Intelligence , Arizona State University"},{"name":"Wei Fan","slug":"wei-fan","given":"Wei","family":"Fan","affiliation":"Medical Sciences Division , University of Oxford"},{"name":"Haifeng Chen","slug":"haifeng-chen","given":"Haifeng","family":"Chen","affiliation":"NEC Laboratories America Inc"},{"name":"Pengyang Wang","slug":"pengyang-wang","given":"Pengyang","family":"Wang","affiliation":"Department of CIS , SKL-IOTSC , University of Macau {solomonhxm ,"},{"name":"Yanjie Fu","slug":"yanjie-fu","given":"Yanjie","family":"Fu","affiliation":"School of Computing and Augmented Intelligence , Arizona State University"}],"abstract":"The Variable Subset Forecasting (VSF) problem, where the majority of variables are unavailable in the inference stage of multivariate forecasting, has been an important but under-explored task with broad impacts in many real-world applications. Missing values, absent inter-correlation, and the impracticality of retraining largely hinder the ability of multivariate forecasting models to capture inherent relationships among variables, impacting their performance. However, existing approaches towards these issues either heavily rely on local temporal correlation or face limitations in fully recovering missing information from the unavailable subset, accompanied by notable computational expenses. To address these problems, we propose a novel density estimation solution to recover the information of missing variables via flows-based generative framework. In particular, a novel generative network for time series, namely Time-series Reconstruction Flows (TRF), is proposed to estimate and reconstruct the missing variable subset. In addition, a novel meta-training framework, Variable-Agnostic Meta Learning, has been developed to enhance the generalization ability of TRF, enabling it to adapt to diverse missing variables situations. Finally, extensive experiments are conducted to demonstrate the superiority of our proposed method compared with baseline methods.","doi":"10.24963/ijcai.2024/228","url":"https://ijcai.org/proceedings/2024/228","pdf":"https://ijcai.org/proceedings/2024/0228.pdf","markdown":"https://ijcai.org/proceedings/2024/228.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/228","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":228,"pages":{"first":2063,"last":2071},"keywords":["Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2018] Shaojie Bai, J Zico Kolter, and Vladlen Koltun. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271, 2018.","title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","authors":"Bai","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bai et al., 2020] Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural infor- mation processing systems, 33:17804-17815, 2020.","title":"Adaptive graph convolutional recurrent network for traffic forecasting","authors":"Lei Bai, Lina Yao, Can Li, Xianzhi Wang, Can Wang","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bartholomew, 1971] David J Bartholomew. Time series analysis forecasting and control., 1971.","title":"Time series analysis forecasting and control","authors":"J David, Bartholomew","year":"1971","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Byrne, 2019] Ruth MJ Byrne. Counterfactuals in explain- able artificial intelligence (xai): Evidence from human rea- soning. In IJCAI, pages 6276-6282, 2019.","title":"Counterfactuals in explainable artificial intelligence (xai): Evidence from human reasoning","authors":"M J Ruth, Byrne","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/876"},{"position":5,"raw":"[Chauhan et al., 2022] Jatin Chauhan, Aravindan Raghu- veer, Rishi Saket, Jay Nandy, and Balaraman Ravindran. Multi-variate time series forecasting on variable subsets. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 76-86, 2022.","title":"Multi-variate time series forecasting on variable subsets","authors":"Jatin Chauhan, Aravindan Raghuveer, Rishi Saket, Jay Nandy, Balaraman Ravindran","year":"2022","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dinh et al., 2014] Laurent Dinh, David Krueger, and Yoshua Bengio. Nice: Non-linear independent compo- nents estimation. arXiv preprint arXiv:1410.8516, 2014.","title":"Nice: Non-linear independent components estimation","authors":"Laurent Dinh, David Krueger, Yoshua Bengio","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dinh et al., 2016] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. arXiv preprint arXiv:1605.08803, 2016.","title":"Density estimation using real nvp","authors":"Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Domeniconi and Yan, 2004] Carlotta Domeniconi and Bo- jun Yan. Nearest neighbor ensemble. In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., volume 1, pages 228-231. IEEE, 2004.","title":"Carlotta Domeniconi and Bojun Yan. Nearest neighbor ensemble","authors":"Yan Domeniconi","year":"2004","venue":"Proceedings of the 17th International Conference on Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Du et al., 2019] Shengdong Du, Tianrui Li, Yan Yang, and Shi-Jinn Horng. Deep air quality forecasting using hybrid deep learning framework. IEEE Transactions on Knowl- edge and Data Engineering, 33(6):2412-2424, 2019.","title":"Deep air quality forecasting using hybrid deep learning framework","authors":"Du","year":"2019","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Du et al., 2023] Wenjie Du, David Côté, and Yan Liu. Saits: Self-attention-based imputation for time series. Expert Systems with Applications, 219:119619, 2023.","title":"Saits: Self-attention-based imputation for time series","authors":"Wenjie Du, David Côté, Yan Liu","year":"2023","venue":"Expert Systems with Applications","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Du Preez and Witt, 2003] Johann Du Preez and Stephen F Witt. Univariate versus multivariate time series forecast- ing: an application to international tourism demand. Inter- national Journal of Forecasting, 19(3):435-451, 2003.","title":"Univariate versus multivariate time series forecasting: an application to international tourism demand","authors":"Du Preez, Johann Du Witt, Stephen F Preez, Witt","year":"2003","venue":"International Journal of Forecasting","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Fan et al., 2022] Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, and Tie-Yan Liu. Depts: Deep expansion learning for periodic time series forecast- ing. arXiv preprint arXiv:2203.07681, 2022.","title":"Depts: Deep expansion learning for periodic time series forecasting","authors":"Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Fan et al., 2023] Wei Fan, Pengyang Wang, Dongkun Wang, Dongjie Wang, Yuanchun Zhou, and Yanjie Fu. Dish-ts: a general paradigm for alleviating distribution shift in time series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 7522-7529, 2023.","title":"Dish-ts: a general paradigm for alleviating distribution shift in time series forecasting","authors":"Wei Fan, Pengyang Wang, Dongkun Wang, Dongjie Wang, Yuanchun Zhou, Yanjie Fu","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Finn et al., 2017] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning, pages 1126-1135. PMLR, 2017.","title":"Model-agnostic meta-learning for fast adaptation of deep networks","authors":"Finn","year":"2017","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Germain et al., 2015] Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle. Made: Masked autoen- coder for distribution estimation. In International confer- ence on machine learning, pages 881-889. PMLR, 2015.","title":"Made: Masked autoencoder for distribution estimation","authors":"Karol Mathieu Germain, Iain Gregor, Hugo Murray, Larochelle","year":"2015","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gen- erative adversarial nets. Advances in neural information processing systems, 27, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Guo et al., 2019] Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. Attention based spatial- temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 922-929, 2019.","title":"Attention based spatialtemporal graph convolutional networks for traffic flow forecasting","authors":"Guo","year":"2019","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Hamid and Wibowo, 2018] Nur Hamid and Wahyu Catur Wibowo. Wind speed forecasting using multivariate time- series radial basis function neural network. In 2018 Inter- national Conference on Advanced Computer Science and Information Systems (ICACSIS), pages 423-428. IEEE, 2018.","title":"Wind speed forecasting using multivariate timeseries radial basis function neural network","authors":"Wibowo ; Hamid, Nur Hamid, Wahyu Catur, Wibowo","year":"2018","venue":"2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Hamzac ¸ebi, 2008] Cos ¸kun Hamzac ¸ebi. Improving artificial neural networks' performance in seasonal time series fore- casting. Information Sciences, 178(23):4550-4559, 2008.","title":"Cos ¸kun Hamzac ¸ebi. Improving artificial neural networks' performance in seasonal time series forecasting","authors":"Hamzac ¸ebi","year":"2008","venue":"Information Sciences","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Hu et al., 2023] Xuanming Hu, Wei Fan, Kun Yi, Pengfei Wang, Yuanbo Xu, Yanjie Fu, and Pengyang Wang. Boost- ing urban prediction via addressing spatial-temporal dis- tribution shift. In 2023 IEEE International Conference on Data Mining (ICDM), pages 160-169. IEEE, 2023.","title":"Boosting urban prediction via addressing spatial-temporal distribution shift","authors":"Xuanming Hu, Wei Fan, Kun Yi, Pengfei Wang, Yuanbo Xu, Yanjie Fu, Pengyang Wang","year":"2023","venue":"IEEE International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Hu et al., 2024] Xuanming Hu, Wei Fan, Dongjie Wang, Pengyang Wang, Yong Li, and Yanjie Fu. Dual-stage flows-based generative modeling for traceable urban plan- ning. In Proceedings of the 2024 SIAM International Con- ference on Data Mining (SDM), pages 370-378. SIAM, 2024.","title":"Dual-stage flows-based generative modeling for traceable urban planning","authors":"Xuanming Hu, Wei Fan, Dongjie Wang, Pengyang Wang, Yong Li, Yanjie Fu","year":"2024","venue":"Proceedings of the 2024 SIAM International Conference on Data Mining (SDM)","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Ioffe and Szegedy, 2015] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep net- work training by reducing internal covariate shift. In International conference on machine learning, pages 448-456. pmlr, 2015.","title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","authors":"Szegedy Ioffe, Sergey Ioffe, Christian Szegedy","year":"2015","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Kingma and Dhariwal, 2018] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 con- volutions. Advances in neural information processing sys- tems, 31, 2018.","title":"Glow: Generative flow with invertible 1x1 convolutions","authors":"Dhariwal ; Kingma, P Durk, Prafulla Kingma, Dhariwal","year":"2018","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Kingma and Welling, 2013] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.","title":"Auto-encoding variational bayes","authors":"P Diederik, Max Kingma, Welling","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Li et al., 2017] Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. Diffusion convolutional recurrent neural net- work: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926, 2017.","title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","authors":"Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Luo et al., 2018] Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, et al. Multivariate time series imputation with generative adversarial networks. Advances in neural information processing systems, 31, 2018.","title":"Multivariate time series imputation with generative adversarial networks","authors":"Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu","year":"2018","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Miao et al., 2021] Xiaoye Miao, Yangyang Wu, Jun Wang, Yunjun Gao, Xudong Mao, and Jianwei Yin. Generative semi-supervised learning for multivariate time series im- putation. In Proceedings of the AAAI conference on artifi- cial intelligence, volume 35, pages 8983-8991, 2021.","title":"Generative semi-supervised learning for multivariate time series imputation","authors":"Xiaoye Miao, Yangyang Wu, Jun Wang, Yunjun Gao, Xudong Mao, Jianwei Yin","year":"2021","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Nichol and Schulman, 2018] Alex Nichol and John Schul- man. Reptile: a scalable metalearning algorithm. arXiv preprint arXiv:1803.02999, 2(3):4, 2018.","title":"Reptile: a scalable metalearning algorithm","authors":"Alex Nichol, John Schulman","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Oreshkin et al., 2019] Boris N Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio. N-beats: Neural basis expansion analysis for interpretable time series fore- casting. arXiv preprint arXiv:1905.10437, 2019.","title":"N-beats: Neural basis expansion analysis for interpretable time series forecasting","authors":"Dmitri Boris N Oreshkin, Nicolas Carpov, Yoshua Chapados, Bengio","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Papamakarios et al., 2017] George Papamakarios, Theo Pavlakou, and Iain Murray. Masked autoregressive flow for density estimation. Advances in neural information processing systems, 30, 2017.","title":"Masked autoregressive flow for density estimation","authors":"Papamakarios","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Ren et al., 2022] Weijieying Ren, Pengyang Wang, Xiaolin Li, Charles E Hughes, and Yanjie Fu. Semi-supervised drifted stream learning with short lookback. In Proceed- ings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1504-1513, 2022.","title":"Semi-supervised drifted stream learning with short lookback","authors":"Ren","year":"2022","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Tang et al., 2020] Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Charu Aggarwal, Prasenjit Mitra, and Suhang Wang. Joint modeling of local and global temporal dynamics for mul- tivariate time series forecasting with missing values. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 34, pages 5956-5963, 2020.","title":"Joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values","authors":"Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Charu Aggarwal, Prasenjit Mitra, Suhang Wang","year":"2020","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Tashiro et al., 2021] Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. Csdi: Conditional score- based diffusion models for probabilistic time series impu- tation. Advances in Neural Information Processing Sys- tems, 34:24804-24816, 2021.","title":"Csdi: Conditional scorebased diffusion models for probabilistic time series imputation","authors":"Yusuke Tashiro, Jiaming Song, Yang Song, Stefano Ermon","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Van Buuren and Groothuis-Oudshoorn, 2011] Stef Van Bu- uren and Karin Groothuis-Oudshoorn. mice: Multivariate imputation by chained equations in r. Journal of statistical software, 45:1-67, 2011.","title":"mice: Multivariate imputation by chained equations in r","authors":"Groothuis-Oudshoorn ; Stef Van Buuren, Karin Van Buuren, Groothuis-Oudshoorn","year":"2011","venue":"Journal of statistical software","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Vanschoren, 2018] Joaquin Vanschoren. Meta-learning: A survey. arXiv preprint arXiv:1810.03548, 2018.","title":"Joaquin Vanschoren. Meta-learning: A survey","authors":"Vanschoren","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wu et al., 2020] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. Con- necting the dots: Multivariate time series forecasting with graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discov- ery & data mining, pages 753-763, 2020.","title":"Connecting the dots: Multivariate time series forecasting with graph neural networks","authors":"Wu","year":"2020","venue":"Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yi et al., 2024a] Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbing Cao, and Zhen- dong Niu. Fouriergnn: Rethinking multivariate time series forecasting from a pure graph perspective. Advances in Neural Information Processing Systems, 36, 2024.","title":"Fouriergnn: Rethinking multivariate time series forecasting from a pure graph perspective","authors":"Yi","year":"2024","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Yi et al., 2024b] Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Ning An, Defu Lian, Longbing Cao, and Zhendong Niu. Frequency-domain mlps are more effective learners in time series forecast- ing. Advances in Neural Information Processing Systems, 36, 2024.","title":"Frequency-domain mlps are more effective learners in time series forecasting","authors":"Yi","year":"2024","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Yick et al., 2008] Jennifer Yick, Biswanath Mukherjee, and Dipak Ghosal. Wireless sensor network survey. Computer networks, 52(12):2292-2330, 2008.","title":"Wireless sensor network survey","authors":"Jennifer Yick, Biswanath Mukherjee, Dipak Ghosal","year":"2008","venue":"Computer networks","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yu et al., 2016] Hsiang-Fu Yu, Nikhil Rao, and Inderjit S Dhillon. Temporal regularized matrix factorization for high-dimensional time series prediction. Advances in neu- ral information processing systems, 29, 2016.","title":"Temporal regularized matrix factorization for high-dimensional time series prediction","authors":"Hsiang-Fu Yu, Nikhil Rao, Inderjit S Dhillon","year":"2016","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zeng et al., 2023] Ailing Zeng, Muxi Chen, Lei Zhang, and Qiang Xu. Are transformers effective for time series fore- casting? In Proceedings of the AAAI conference on artifi- cial intelligence, volume 37, pages 11121-11128, 2023.","title":"Are transformers effective for time series forecasting?","authors":"Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu","year":"2023","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhao et al., 2019] Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. T-gcn: A temporal graph convolutional network for traffic prediction. IEEE transactions on intelligent transportation systems, 21(9):3848-3858, 2019.","title":"T-gcn: A temporal graph convolutional network for traffic prediction","authors":"Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, Haifeng Li","year":"2019","venue":"IEEE transactions on intelligent transportation systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}