{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T23:16:15Z","timestamp":1793142975022,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":11,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"The Science and Technology Planning Project of Shenzhen Municipality","award":["JCYJ20190806112210067"],"award-info":[{"award-number":["JCYJ20190806112210067"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,26]]},"DOI":"10.1145\/3459637.3482054","type":"proceedings-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T15:45:32Z","timestamp":1636991132000},"page":"3439-3442","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["AGCNT"],"prefix":"10.1145","author":[{"given":"Hongyang","family":"Su","sequence":"first","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shen Zhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Wang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shen Zhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Qin","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shen Zhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. arXiv preprint arXiv:2007.02842","author":"Bai Lei","year":"2020","unstructured":"Lei Bai , Lina Yao , Can Li , Xianzhi Wang , and Can Wang . 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. arXiv preprint arXiv:2007.02842 ( 2020 ). Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. arXiv preprint arXiv:2007.02842 (2020)."},{"key":"e_1_3_2_2_2_1","volume-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung , Caglar Gulcehre , KyungHyun Cho , and Yoshua Bengio . 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 ( 2014 ). Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294869"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_2_5_1","volume-title":"Reformer: The efficient transformer. arXiv preprint arXiv:2001.04451","author":"Kitaev Nikita","year":"2020","unstructured":"Nikita Kitaev , \u0141ukasz Kaiser , and Anselm Levskaya . 2020 . Reformer: The efficient transformer. arXiv preprint arXiv:2001.04451 (2020). Nikita Kitaev, \u0141ukasz Kaiser, and Anselm Levskaya. 2020. Reformer: The efficient transformer. arXiv preprint arXiv:2001.04451 (2020)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454758"},{"key":"e_1_3_2_2_8_1","volume-title":"Attention is all you need. arXiv preprint arXiv:1706.03762","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , Lukasz Kaiser , and Illia Polosukhin . 2017. Attention is all you need. arXiv preprint arXiv:1706.03762 ( 2017 ). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"e_1_3_2_2_10_1","volume-title":"Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. arXiv preprint arXiv:2012.07436","author":"Zhou Haoyi","year":"2020","unstructured":"Haoyi Zhou , Shanghang Zhang , Jieqi Peng , Shuai Zhang , Jianxin Li , Hui Xiong , and Wancai Zhang . 2020 . Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. arXiv preprint arXiv:2012.07436 (2020). Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. 2020. Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. arXiv preprint arXiv:2012.07436 (2020)."},{"key":"e_1_3_2_2_11_1","volume-title":"Vector autoregressive models for multivariate time series. Modeling Financial Time Series with S-Plus\u00ae","author":"Zivot Eric","year":"2006","unstructured":"Eric Zivot and Jiahui Wang . 2006. Vector autoregressive models for multivariate time series. Modeling Financial Time Series with S-Plus\u00ae ( 2006 ), 385--429. Eric Zivot and Jiahui Wang. 2006. Vector autoregressive models for multivariate time series. Modeling Financial Time Series with S-Plus\u00ae (2006), 385--429."}],"event":{"name":"CIKM '21: The 30th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Queensland Australia","acronym":"CIKM '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 30th ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482054","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3482054","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:12Z","timestamp":1750188612000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482054"}},"subtitle":["Adaptive Graph Convolutional Network for Transformer-based Long Sequence Time-Series Forecasting"],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":11,"alternative-id":["10.1145\/3459637.3482054","10.1145\/3459637"],"URL":"https:\/\/doi.org\/10.1145\/3459637.3482054","relation":{},"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"2021-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}