{"type":"ScholarlyArticle","title":"Learning Gaussian Mixture Representations for Tensor Time Series Forecasting","authors":[{"name":"Jiewen Deng","slug":"jiewen-deng","given":"Jiewen","family":"Deng","affiliation":"Southern University of Science and Technology"},{"name":"Jinliang Deng","slug":"jinliang-deng","given":"Jinliang","family":"Deng","affiliation":"Southern University of Science and Technology; University of Technology Sydney"},{"name":"Renhe Jiang","slug":"renhe-jiang","given":"Renhe","family":"Jiang","affiliation":"The University of Tokyo; Southern University of Science and Technology"},{"name":"Xuan Song","slug":"xuan-song","given":"Xuan","family":"Song","affiliation":"Southern University of Science and Technology; The University of Tokyo"}],"abstract":"Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems involving multi-source spatio-temporal data (e.g., transportation demands and air pollutants). Compared to modeling time series or multivariate time series, which has received much attention and achieved tremendous progress in recent years, tensor time series has been paid less effort. Properly coping with the tensor time series is a much more challenging task, due to its high-dimensional and complex inner structure. In this paper, we develop a novel TTS forecasting framework, which seeks to individually model each heterogeneity component implied in the time, the location, and the source variables. We name this framework as GMRL, short for Gaussian Mixture Representation Learning. Experiment results on two real-world TTS datasets verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/beginner-sketch/GMRL.","doi":"10.24963/ijcai.2023/231","url":"https://ijcai.org/proceedings/2023/231","pdf":"https://ijcai.org/proceedings/2023/0231.pdf","markdown":"https://ijcai.org/proceedings/2023/231.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/231","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":231,"pages":{"first":2077,"last":2085},"keywords":["Knowledge Representation and Reasoning: KRR: Qualitative, geometric, spatial, and temporal reasoning","Machine Learning: ML: Time series and data streams","Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[{"position":1,"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":2,"raw":"[Cao et al., 2020] Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bix- iong Xu, Jing Bai, Jie Tong, et al. Spectral temporal graph neural network for multivariate time-series forecast- ing. Advances in neural information processing systems, 33:17766-17778, 2020.","title":"Spectral temporal graph neural network for multivariate time-series forecasting","authors":"Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2020] Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, and Xiaojie Feng. Multi-range atten- tive bicomponent graph convolutional network for traffic forecasting. In Proceedings of the AAAI conference on ar- tificial intelligence, volume 34, pages 3529-3536, 2020.","title":"Multi-range attentive bicomponent graph convolutional network for traffic forecasting","authors":"Weiqi Chen, Ling Chen, Yu Xie, Wei Cao, Yusong Gao, Xiaojie Feng","year":"2020","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Deng et al., 2021] Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, and Ivor W Tsang. St-norm: Spatial and tem- poral normalization for multi-variate time series forecast- ing. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pages 269-278, 2021.","title":"St-norm: Spatial and temporal normalization for multi-variate time series forecasting","authors":"Deng","year":"2021","venue":"Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Deng et al., 2022a] Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, and Ivor W Tsang. A multi-view multi- task learning framework for multi-variate time series fore- casting. IEEE Transactions on Knowledge and Data En- gineering, 2022.","title":"A multi-view multitask learning framework for multi-variate time series forecasting","authors":"Deng","year":"2022","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Deng et al., 2022b] Leyan Deng, Defu Lian, Zhenya Huang, and Enhong Chen. Graph convolutional adversar- ial networks for spatiotemporal anomaly detection. IEEE Transactions on Neural Networks and Learning Systems, 33(6):2416-2428, 2022.","title":"Graph convolutional adversarial networks for spatiotemporal anomaly detection","authors":"Deng","year":"2022","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Duan et al., 2022] Jufang Duan, Yi Wang, and Wei Zheng. Plae: Time-series prediction improvement by adaptive de- composition. In Pacific Rim International Conference on Artificial Intelligence, pages 394-407. Springer, 2022.","title":"Plae: Time-series prediction improvement by adaptive decomposition","authors":"Duan","year":"2022","venue":"Pacific Rim International Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Fang et al., 2019] Shen Fang, Qi Zhang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. Gstnet: Global spatial-temporal network for traffic flow prediction. In IJ- CAI, pages 2286-2293, 2019.","title":"Gstnet: Global spatial-temporal network for traffic flow prediction","authors":"Shen Fang, Qi Zhang, Gaofeng Meng, Shiming Xiang, Chunhong Pan","year":"2019","venue":"IJ-CAI","doi":null,"url":null,"internal_url":"/proceedings/2019/317"},{"position":9,"raw":"[Fang et al., 2021] Ziquan Fang, Lu Pan, Lu Chen, Yuntao Du, and Yunjun Gao. Mdtp: a multi-source deep traffic prediction framework over spatio-temporal trajectory data. Proceedings of the VLDB Endowment, 14(8):1289-1297, 2021.","title":"Mdtp: a multi-source deep traffic prediction framework over spatio-temporal trajectory data","authors":"Fang","year":"2021","venue":"Proceedings of the VLDB Endowment","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Guo et al., 2019] Shengnan Guo, Youfang Lin, Shijie Li, Zhaoming Chen, and Huaiyu Wan. Deep spatial-temporal 3d convolutional neural networks for traffic data forecast- ing. IEEE Transactions on Intelligent Transportation Sys- tems, 20(10):3913-3926, 2019.","title":"Deep spatial-temporal 3d convolutional neural networks for traffic data forecasting","authors":"Guo","year":"2019","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Guo et al., 2021] Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, and Baocai Yin. Hierarchical graph convolution network for traffic forecasting. In Proceed- ings of the AAAI Conference on Artificial Intelligence, vol- ume 35, pages 151-159, 2021.","title":"Hierarchical graph convolution network for traffic forecasting","authors":"Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, Baocai Yin","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Han et al., 2021] Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, and Hui Xiong. Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 547-555, 2021.","title":"Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting","authors":"Han","year":"2021","venue":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Huang et al., 2019] Chao Huang, Chuxu Zhang, Jiashu Zhao, Xian Wu, Dawei Yin, and Nitesh Chawla. Mist: A multiview and multimodal spatial-temporal learning framework for citywide abnormal event forecasting. In The World Wide Web Conference, pages 717-728, 2019.","title":"Mist: A multiview and multimodal spatial-temporal learning framework for citywide abnormal event forecasting","authors":"Chao Huang, Chuxu Zhang, Jiashu Zhao, Xian Wu, Dawei Yin, Nitesh Chawla","year":"2019","venue":"The World Wide Web Conference","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Huang et al., 2021] Chao Huang, Chuxu Zhang, Peng Dai, and Liefeng Bo. Cross-interaction hierarchical attention networks for urban anomaly prediction. In Proceedings of the Twenty-Ninth International Conference on Interna- tional Joint Conferences on Artificial Intelligence, pages 4359-4365, 2021.","title":"Cross-interaction hierarchical attention networks for urban anomaly prediction","authors":"Chao Huang, Chuxu Zhang, Peng Dai, Liefeng Bo","year":"2021","venue":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2020/601"},{"position":15,"raw":"[Jiang et al., 2021] Renhe Jiang, Du Yin, Zhaonan Wang, Yizhuo Wang, Jiewen Deng, Hangchen Liu, Zekun Cai, Jinliang Deng, Xuan Song, and Ryosuke Shibasaki. Dl- traff: Survey and benchmark of deep learning models for urban traffic prediction. In Proceedings of the 30th ACM international conference on information & knowl- edge management, pages 4515-4525, 2021.","title":"Dltraff: Survey and benchmark of deep learning models for urban traffic prediction","authors":"Renhe Jiang, Du Yin, Zhaonan Wang, Yizhuo Wang, Jiewen Deng, Hangchen Liu, Zekun Cai, Jinliang Deng, Xuan Song, Ryosuke Shibasaki","year":"2021","venue":"Proceedings of the 30th ACM international conference on information & knowledge management","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jiang et al., 2022] Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, and Toyotaro Suzumura. Spatio-temporal meta-graph learning for traffic forecast- ing. arXiv preprint arXiv:2211.14701, 2022.","title":"Spatio-temporal meta-graph learning for traffic forecasting","authors":"Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, Toyotaro Suzumura","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Jiang et al., 2023] Renhe Jiang, Zhaonan Wang, Yudong Tao, Chuang Yang, Xuan Song, Ryosuke Shibasaki, Shu- Ching Chen, and Mei-Ling Shyu. Learning social meta- knowledge for nowcasting human mobility in disaster. In Proceedings of the ACM Web Conference 2023, pages 2655-2665, 2023.","title":"Learning social metaknowledge for nowcasting human mobility in disaster","authors":"Renhe Jiang, Zhaonan Wang, Yudong Tao, Chuang Yang, Xuan Song, Ryosuke Shibasaki, Shu-Ching Chen, Mei-Ling Shyu","year":"2023","venue":"Proceedings of the ACM Web Conference 2023","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Jing et al., 2021] Baoyu Jing, Hanghang Tong, and Yada Zhu. Network of tensor time series. In Proceedings of the Web Conference 2021, pages 2425-2437, 2021.","title":"Network of tensor time series","authors":"Jing","year":"2021","venue":"Proceedings of the Web Conference 2021","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lai et al., 2018] Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu. Modeling long-and short-term temporal patterns with deep neural networks. In The 41st international ACM SIGIR conference on research & devel- opment in information retrieval, pages 95-104, 2018.","title":"Modeling long-and short-term temporal patterns with deep neural networks","authors":"Lai","year":"2018","venue":"The 41st international ACM SIGIR conference on research & development in information retrieval","doi":null,"url":null,"internal_url":null},{"position":20,"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":21,"raw":"[Li et al., 2019] Shiyang Li, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan. En- hancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neu- ral information processing systems, 32, 2019.","title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","authors":"Shiyang Li, Xiaoyong Jin, Xiyou Yao Xuan, Wenhu Zhou, Yu-Xiang Chen, Xifeng Wang, Yan","year":"2019","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2021] Shizhan Liu, Hang Yu, Cong Liao, Jian- guo Li, Weiyao Lin, Alex X Liu, and Schahram Dust- dar. Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. In Inter- national conference on learning representations, 2021.","title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting","authors":"Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X Liu, Schahram Dustdar","year":"2021","venue":"International conference on learning representations","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Oreshkin et al., 2021] Boris N Oreshkin, Arezou Amini, Lucy Coyle, and Mark Coates. Fc-gaga: Fully connected gated graph architecture for spatio-temporal traffic fore- casting. In Proceedings of the AAAI Conference on Artifi- cial Intelligence, volume 35, pages 9233-9241, 2021.","title":"Fc-gaga: Fully connected gated graph architecture for spatio-temporal traffic forecasting","authors":"Arezou Boris N Oreshkin, Lucy Amini, Mark Coyle, Coates","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Pan et al., 2019] Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, and Junbo Zhang. Urban traffic pre- diction from spatio-temporal data using deep meta learn- ing. In Proceedings of the 25th ACM SIGKDD interna- tional conference on knowledge discovery & data mining, pages 1720-1730, 2019.","title":"Urban traffic prediction from spatio-temporal data using deep meta learning","authors":"Pan","year":"2019","venue":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Rogers et al., 2013] Mark Rogers, Lei Li, and Stuart J Rus- sell. Multilinear dynamical systems for tensor time series. Advances in Neural Information Processing Systems, 26, 2013.","title":"Multilinear dynamical systems for tensor time series","authors":"Mark Rogers, Lei Li, Stuart J Russell","year":"2013","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Shang et al., 2021] Chao Shang, Jie Chen, and Jinbo Bi. Discrete graph structure learning for forecasting multiple time series. arXiv preprint arXiv:2101.06861, 2021.","title":"Discrete graph structure learning for forecasting multiple time series","authors":"Chao Shang, Jie Chen, Jinbo Bi","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Shao et al., 2022] Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, and Christian S Jensen. De- coupled dynamic spatial-temporal graph neural network for traffic forecasting, 2022.","title":"Decoupled dynamic spatial-temporal graph neural network for traffic forecasting","authors":"Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, Christian S Jensen","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[van den Oord et al., 2016] Aäron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew W. Senior, and Koray Kavukcuoglu. Wavenet: A generative model for raw audio. In SSW, 2016.","title":"Wavenet: A generative model for raw audio","authors":"Oord Van Den","year":"2016","venue":"SSW","doi":null,"url":null,"internal_url":null},{"position":29,"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":30,"raw":"[Wang et al., 2021] Zhaonan Wang, Renhe Jiang, Zekun Cai, Zipei Fan, Xin Liu, Kyoung-Sook Kim, Xuan Song, and Ryosuke Shibasaki. Spatio-temporal-categorical graph neural networks for fine-grained multi-incident co- prediction. In Proceedings of the 30th ACM interna- tional conference on information & knowledge manage- ment, pages 2060-2069, 2021.","title":"Spatio-temporal-categorical graph neural networks for fine-grained multi-incident coprediction","authors":"Wang","year":"2021","venue":"Proceedings of the 30th ACM international conference on information & knowledge management","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2022] Zhaonan Wang, Renhe Jiang, Hao Xue, Flora D Salim, Xuan Song, and Ryosuke Shibasaki. Event- aware multimodal mobility nowcasting. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 4228-4236, 2022.","title":"Eventaware multimodal mobility nowcasting","authors":"Wang","year":"2022","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wu et al., 2019] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121, 2019.","title":"Graph wavenet for deep spatial-temporal graph modeling","authors":"Wu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2019/264"},{"position":33,"raw":"[Wu et al., 2020a] Xian Wu, Chao Huang, Chuxu Zhang, and Nitesh V Chawla. Hierarchically structured trans- former networks for fine-grained spatial event forecasting. In Proceedings of The Web Conference 2020, pages 2320- 2330, 2020.","title":"Hierarchically structured transformer networks for fine-grained spatial event forecasting","authors":"Wu","year":"2020","venue":"Proceedings of The Web Conference 2020","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wu et al., 2020b] 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":35,"raw":"[Wu et al., 2021] Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. Autoformer: Decomposition transform- ers with auto-correlation for long-term series forecast- ing. Advances in Neural Information Processing Systems, 34:22419-22430, 2021.","title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","authors":"Wu","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yang et al., 2021] Song Yang, Jiamou Liu, and Kaiqi Zhao. Space meets time: Local spacetime neural network for traffic flow forecasting. In 2021 IEEE International Con- ference on Data Mining (ICDM), pages 817-826. IEEE, 2021.","title":"Space meets time: Local spacetime neural network for traffic flow forecasting","authors":"Song Yang, Jiamou Liu, Kaiqi Zhao","year":"2021","venue":"IEEE International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Ye et al., 2019] Junchen Ye, Leilei Sun, Bowen Du, Yan- jie Fu, Xinran Tong, and Hui Xiong. Co-prediction of multiple transportation demands based on deep spatio- temporal neural network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discov- ery & Data Mining, pages 305-313, 2019.","title":"Co-prediction of multiple transportation demands based on deep spatiotemporal neural network","authors":"Ye","year":"2019","venue":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yu et al., 2017] Bing Yu, Haoteng Yin, and Zhanxing Zhu. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875, 2017.","title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","authors":"Yu","year":"2017","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2018/505"},{"position":39,"raw":"[Zhang et al., 2016] Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, and Xiuwen Yi. Dnn-based prediction model for spatio-temporal data. In Proceedings of the 24th ACM SIGSPATIAL international conference on advances in ge- ographic information systems, pages 1-4, 2016.","title":"Dnn-based prediction model for spatio-temporal data","authors":"Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi","year":"2016","venue":"Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Zhang et al., 2017] Junbo Zhang, Yu Zheng, and Dekang Qi. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Thirty-first AAAI conference on artificial intelligence, 2017.","title":"Deep spatio-temporal residual networks for citywide crowd flows prediction","authors":"Junbo Zhang, Yu Zheng, Dekang Qi","year":"2017","venue":"Thirty-first AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Zhao et al., 2018] Tiancheng Zhao, Kyusong Lee, and Max- ine Eskenazi. Unsupervised discrete sentence represen- tation learning for interpretable neural dialog generation. arXiv preprint arXiv:1804.08069, 2018.","title":"Unsupervised discrete sentence representation learning for interpretable neural dialog generation","authors":"Zhao","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zheng et al., 2020] Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. Gman: A graph multi- attention network for traffic prediction. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 1234-1241, 2020.","title":"Gman: A graph multiattention network for traffic prediction","authors":"Zheng","year":"2020","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhou et al., 2021] Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 11106-11115, 2021.","title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","authors":"Zhou","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zhou et al., 2022] Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. Fedformer: Fre- quency enhanced decomposed transformer for long-term series forecasting. In International Conference on Ma- chine Learning, pages 27268-27286. PMLR, 2022.","title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","authors":"Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Zhu et al., 2021] Zhibo Zhu, Ziqi Liu, Ge Jin, Zhiqiang Zhang, Lei Chen, Jun Zhou, and Jianyong Zhou. Mixseq: Connecting macroscopic time series forecasting with mi- croscopic time series data. Advances in Neural Informa- tion Processing Systems, 34:12904-12916, 2021.","title":"Mixseq: Connecting macroscopic time series forecasting with microscopic time series data","authors":"Zhibo Zhu, Ziqi Liu, Ge Jin, Zhiqiang Zhang, Lei Chen, Jun Zhou, Jianyong Zhou","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}