{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T04:52:11Z","timestamp":1773550331968,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402531, 623B2043"],"award-info":[{"award-number":["62402531, 623B2043"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,28]]},"DOI":"10.1145\/3696410.3714514","type":"proceedings-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T22:47:11Z","timestamp":1745362031000},"page":"5054-5064","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7656-445X","authenticated-orcid":false,"given":"Wei","family":"Fan","sequence":"first","affiliation":[{"name":"University of Oxford, Oxford, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8731-1860","authenticated-orcid":false,"given":"Jingru","family":"Fei","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3053-604X","authenticated-orcid":false,"given":"Dingyu","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Chicago, Chicago, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9980-6033","authenticated-orcid":false,"given":"Kun","family":"Yi","sequence":"additional","affiliation":[{"name":"State Information Center of China, North China Institute of Computing Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7861-8957","authenticated-orcid":false,"given":"Xiaozhuang","family":"Song","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shenzhe, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4565-8829","authenticated-orcid":false,"given":"Haolong","family":"Xiang","sequence":"additional","affiliation":[{"name":"Nanjing University of Information Science and Technology, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6920-4181","authenticated-orcid":false,"given":"Hangting","family":"Ye","sequence":"additional","affiliation":[{"name":"Jilin University, Changchun, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-1394","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University & Xiangjiang Laboratory & Furong Laboratory, Central South University, Changsha, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105565"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-016-0483-9"},{"key":"e_1_3_2_1_3_1","unstructured":"Lei Bai Lina Yao Can Li Xianzhi Wang and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In NeurIPS."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2018.03.003"},{"key":"e_1_3_2_1_5_1","volume-title":"ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in biology and medicine","author":"Blanco-Velasco Manuel","year":"2008","unstructured":"Manuel Blanco-Velasco, Binwei Weng, and Kenneth E Barner. 2008. ECG signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in biology and medicine, Vol. 38, 1 (2008), 1--13."},{"key":"e_1_3_2_1_6_1","first-page":"24","article-title":"Binary Shapelet Transform for Multiclass Time Series","volume":"32","author":"Bostrom Aaron","year":"2017","unstructured":"Aaron Bostrom and Anthony J. Bagnall. 2017. Binary Shapelet Transform for Multiclass Time Series Classification. Trans. Large Scale Data Knowl. Centered Syst., Vol. 32 (2017), 24--46.","journal-title":"Classification. Trans. Large Scale Data Knowl. Centered Syst."},{"key":"e_1_3_2_1_7_1","volume-title":"Where does EEG come from and what does it mean? Trends in neurosciences","author":"Cohen Michael X","year":"2017","unstructured":"Michael X Cohen. 2017. Where does EEG come from and what does it mean? Trends in neurosciences, Vol. 40, 4 (2017), 208--218."},{"key":"e_1_3_2_1_8_1","volume-title":"Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy. Physiological measurement","author":"Escudero J","year":"2006","unstructured":"J Escudero, Daniel Ab\u00e1solo, Roberto Hornero, Pedro Espino, and Miguel L\u00f3pez. 2006. Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy. Physiological measurement, Vol. 27, 11 (2006), 1091."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-019-00619-1"},{"key":"e_1_3_2_1_10_1","first-page":"46885","article-title":"Crossgnn: Confronting noisy multivariate time series via cross interaction refinement","volume":"36","author":"Huang Qihe","year":"2023","unstructured":"Qihe Huang, Lei Shen, Ruixin Zhang, Shouhong Ding, Binwu Wang, Zhengyang Zhou, and Yang Wang. 2023. Crossgnn: Confronting noisy multivariate time series via cross interaction refinement. Advances in Neural Information Processing Systems, Vol. 36 (2023), 46885--46902.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107450"},{"key":"e_1_3_2_1_12_1","volume-title":"Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, and Shirui Pan.","author":"Jin Ming","year":"2023","unstructured":"Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, and Shirui Pan. 2023. A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection. CoRR, Vol. abs\/2307.03759 (2023)."},{"key":"e_1_3_2_1_13_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_14_1","volume-title":"Clifton","author":"Kiyasseh Dani","year":"2021","unstructured":"Dani Kiyasseh, Tingting Zhu, and David A. Clifton. 2021. CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients. In ICML (Proceedings of Machine Learning Research, Vol. 139). PMLR, 5606--5615."},{"key":"e_1_3_2_1_15_1","volume-title":"Lance","author":"Lawhern Vernon J.","year":"2016","unstructured":"Vernon J. Lawhern, Amelia J. Solon, Nicholas R. Waytowich, Stephen M. Gordon, Chou P. Hung, and Brent J. Lance. 2016. EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces. CoRR, Vol. abs\/1611.08024 (2016)."},{"key":"e_1_3_2_1_16_1","unstructured":"Yaguang Li Rose Yu Cyrus Shahabi and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In ICLR (Poster)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0361-2"},{"key":"e_1_3_2_1_18_1","volume-title":"Todynet: temporal dynamic graph neural network for multivariate time series classification. Information Sciences","author":"Liu Huaiyuan","year":"2024","unstructured":"Huaiyuan Liu, Donghua Yang, Xianzhang Liu, Xinglei Chen, Zhiyu Liang, Hongzhi Wang, Yong Cui, and Jun Gu. 2024b. Todynet: temporal dynamic graph neural network for multivariate time series classification. Information Sciences (2024), 120914."},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Learning Representations","author":"Liu Yong","year":"2024","unstructured":"Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, and Mingsheng Long. 2024a. itransformer: Inverted transformers are effective for time series forecasting. International Conference on Learning Representations (2024)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/data8060095"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531326"},{"key":"e_1_3_2_1_22_1","volume-title":"Sid-Ahmed Selouani, Yasser F. Alharbi, and Yousef Ajami Alotaibi.","author":"Moy Valentin","year":"2021","unstructured":"Valentin Moy, Mohammed Sidi Yakoub, Sid-Ahmed Selouani, Yasser F. Alharbi, and Yousef Ajami Alotaibi. 2021. Classification of the Neural Correlates of Mind Wandering States in EEG Signals. In TSP. IEEE, 126--129."},{"key":"e_1_3_2_1_23_1","volume-title":"A time series is worth 64 words: Long-term forecasting with transformers. ICLR","author":"Nie Yuqi","year":"2023","unstructured":"Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam. 2023. A time series is worth 64 words: Long-term forecasting with transformers. ICLR (2023)."},{"key":"e_1_3_2_1_24_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_25_1","first-page":"e215","article-title":"Physionet: components of a new research resource for complex physiologic signals","volume":"101","author":"PhysioBank PhysioToolkit","year":"2000","unstructured":"PhysioToolkit PhysioBank. 2000. Physionet: components of a new research resource for complex physiologic signals. Circulation, Vol. 101, 23 (2000), e215--e220.","journal-title":"Circulation"},{"key":"e_1_3_2_1_26_1","volume-title":"Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC medical research methodology","author":"Schaffer Andrea L","year":"2021","unstructured":"Andrea L Schaffer, Timothy A Dobbins, and Sallie-Anne Pearson. 2021. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC medical research methodology, Vol. 21 (2021), 1--12."},{"key":"e_1_3_2_1_27_1","volume-title":"Graph Transformers: A Survey. CoRR","author":"Shehzad Ahsan","year":"2024","unstructured":"Ahsan Shehzad, Feng Xia, Shagufta Abid, Ciyuan Peng, Shuo Yu, Dongyu Zhang, and Karin Verspoor. 2024. Graph Transformers: A Survey. CoRR, Vol. abs\/2407.09777 (2024)."},{"key":"e_1_3_2_1_28_1","unstructured":"Michal Teplan et al. 2002. Fundamentals of EEG measurement. Measurement science review Vol. 2 2 (2002) 1--11."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2020.02.006"},{"key":"e_1_3_2_1_30_1","volume-title":"The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database. Scientific data","author":"van Dijk Hanneke","year":"2022","unstructured":"Hanneke van Dijk, Guido van Wingen, Damiaan Denys, Sebastian Olbrich, Rosalinde van Ruth, and Martijn Arns. 2022. The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database. Scientific data, Vol. 9, 1 (2022), 333."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2042064"},{"key":"e_1_3_2_1_32_1","volume-title":"a large publicly available electrocardiography dataset. Scientific data","author":"Wagner Patrick","year":"2020","unstructured":"Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Dieter Kreiseler, Fatima I Lunze, Wojciech Samek, and Tobias Schaeffter. 2020. PTB-XL, a large publicly available electrocardiography dataset. Scientific data, Vol. 7, 1 (2020), 1--15."},{"key":"e_1_3_2_1_33_1","volume-title":"Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series. In NeurIPS.","author":"Wang Yihe","year":"2023","unstructured":"Yihe Wang, Yu Han, Haishuai Wang, and Xiang Zhang. 2023a. Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series. In NeurIPS."},{"key":"e_1_3_2_1_34_1","volume-title":"Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification. CoRR","author":"Wang Yihe","year":"2024","unstructured":"Yihe Wang, Nan Huang, Taida Li, Yujun Yan, and Xiang Zhang. 2024a. Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification. CoRR, Vol. abs\/2405.19363 (2024)."},{"key":"e_1_3_2_1_35_1","volume-title":"Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification. arXiv preprint arXiv:2405.19363","author":"Wang Yihe","year":"2024","unstructured":"Yihe Wang, Nan Huang, Taida Li, Yujun Yan, and Xiang Zhang. 2024b. Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification. arXiv preprint arXiv:2405.19363 (2024)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106641"},{"key":"e_1_3_2_1_37_1","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu Haixu","year":"2021","unstructured":"Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long. 2021. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems, Vol. 34 (2021), 22419--22430.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_38_1","unstructured":"Zonghan Wu Shirui Pan Guodong Long Jing Jiang Xiaojun Chang and Chengqi Zhang. 2020. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. In KDD. 753--763."},{"key":"e_1_3_2_1_39_1","unstructured":"Chaoqi Yang M. Brandon Westover and Jimeng Sun. 2023. ManyDG: Many-domain Generalization for Healthcare Applications. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_40_1","volume-title":"FilterNet: Harnessing Frequency Filters for Time Series Forecasting. arXiv preprint arXiv:2411.01623","author":"Yi Kun","year":"2024","unstructured":"Kun Yi, Jingru Fei, Qi Zhang, Hui He, Shufeng Hao, Defu Lian, and Wei Fan. 2024a. FilterNet: Harnessing Frequency Filters for Time Series Forecasting. arXiv preprint arXiv:2411.01623 (2024)."},{"key":"e_1_3_2_1_41_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Yi Kun","year":"2024","unstructured":"Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbing Cao, and Zhendong Niu. 2024b. FourierGNN: Rethinking multivariate time series forecasting from a pure graph perspective. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_42_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Yi Kun","year":"2024","unstructured":"Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Ning An, Defu Lian, Longbing Cao, and Zhendong Niu. 2024c. Frequency-domain MLPs are more effective learners in time series forecasting. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110486"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Bing Yu Haoteng Yin and Zhanxing Zhu. 2018. Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In IJCAI. 3634--3640.","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_1_45_1","volume-title":"Towards Similarity-Aware Time-Series Classification","author":"Zha Daochen","unstructured":"Daochen Zha, Kwei-Herng Lai, Kaixiong Zhou, and Xia Hu. 2022a. Towards Similarity-Aware Time-Series Classification. In SDM. SIAM, 199--207."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977172.23"},{"key":"e_1_3_2_1_47_1","unstructured":"Xiang Zhang Marko Zeman Theodoros Tsiligkaridis and Marinka Zitnik. 2022. Graph-Guided Network for Irregularly Sampled Multivariate Time Series. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_48_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Zhang Yunhao","year":"2022","unstructured":"Yunhao Zhang and Junchi Yan. 2022. Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_49_1","volume-title":"International Conference on Machine Learning. PMLR, 27268--27286","author":"Zhou Tian","year":"2022","unstructured":"Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin. 2022. Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International Conference on Machine Learning. PMLR, 27268--27286."}],"event":{"name":"WWW '25: The ACM Web Conference 2025","location":"Sydney NSW Australia","acronym":"WWW '25","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714514","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696410.3714514","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:32Z","timestamp":1750295912000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":49,"alternative-id":["10.1145\/3696410.3714514","10.1145\/3696410"],"URL":"https:\/\/doi.org\/10.1145\/3696410.3714514","relation":{},"subject":[],"published":{"date-parts":[[2025,4,22]]},"assertion":[{"value":"2025-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}