{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T08:11:38Z","timestamp":1772007098565,"version":"3.50.1"},"reference-count":61,"publisher":"Association for Computing Machinery (ACM)","issue":"9","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Fund for Distinguished Young Scholars","award":["62025205"],"award-info":[{"award-number":["62025205"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2019QY0600"],"award-info":[{"award-number":["2019QY0600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62032020"],"award-info":[{"award-number":["62032020"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2022JQ-623"],"award-info":[{"award-number":["2022JQ-623"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>Air pollution seriously affects public health, while effective air quality prediction remains a challenging problem since the complex spatial-temporal couplings exist in multi-area monitoring data of the city. Current approaches rarely consider relative geographical locations when capturing spatial-temporal relations, instead the latent inter-dependencies (i.e., implicit spatial relations) of data as a replacement. However, such relations cannot necessarily reflect the diffusion of air pollutants in the real world, and genuine location-related information could be lost during the implicit relation learning process. In this article, we introduce a new concept, geospatial-temporal data, and propose a novel deep neural network architecture, CoupledGT, to learn the geospatial-temporal couplings within data for air quality prediction. Specifically, the asymmetric diffusion relation of air quality data between two areas is first explicitly represented by the newly developed planar Gaussian diffusion (PGD) equation. And then, a geospatial couplings diffuser (GCD) is designed to parameterize the PGD equation and learn multi-areas diffusion mutually affected geospatial couplings. Besides, the RNN is employed to capture temporal couplings of each area, and incorporated with GCD to learn both shared and unique characteristics of the geospatial-temporal data simultaneously, which empowers the generalization and efficiency of the model. Extensive experiments on two real-world datasets demonstrate our method is robust and outperforms existing baseline methods in air quality prediction tasks.<\/jats:p>","DOI":"10.1145\/3604616","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T15:37:19Z","timestamp":1687189039000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["CoupledGT: Coupled Geospatial-temporal Data Modeling for Air Quality Prediction"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9265-3161","authenticated-orcid":false,"given":"Siyuan","family":"Ren","sequence":"first","affiliation":[{"name":"Northwestern Polytechnical University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6097-2467","authenticated-orcid":false,"given":"Bin","family":"Guo","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8372-0350","authenticated-orcid":false,"given":"Ke","family":"Li","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1682-910X","authenticated-orcid":false,"given":"Qianru","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1658-9298","authenticated-orcid":false,"given":"Qinfen","family":"Wang","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9905-3238","authenticated-orcid":false,"given":"Zhiwen","family":"Yu","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University"}]}],"member":"320","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.2307\/143141"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2014.08.007"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2022.3194618"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3542605"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1352-2310(01)00493-9"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10666-007-9106-6"},{"key":"e_1_3_2_8_2","unstructured":"J\u00fcrgen P\u00e4sler-Sauer. 2000. Description of the atmospheric dispersion model ATSTEP. RODOS (WG2)-TN (99)-11 Progress report of RODOS Aug. 2000 FZK Karlsruhe Germany . www.RODOS.fzk.de."},{"key":"e_1_3_2_9_2","unstructured":"Pankaj Malhotra Anusha Ramakrishnan Gaurangi Anand Lovekesh Vig Puneet Agarwal and Gautam Shroff. 2016. LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv:1607.00148. Retrieved from https:\/\/arxiv.org\/abs\/1607.00148."},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.05.028"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Jia Wang Tong Sun Benyuan Liu Yu Cao and Hongwei Zhu. 2019. CLVSA: A convolutional LSTM based variational sequence-to-sequence model with attention for predicting trends of financial markets. In Proceedings of the 28th International Joint Conference on Artificial Intelligence . 3705\u20133711.","DOI":"10.24963\/ijcai.2019\/514"},{"key":"e_1_3_2_12_2","first-page":"5998","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems. 5998\u20136008."},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/476"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358132"},{"key":"e_1_3_2_15_2","unstructured":"Aaron van den Oord Sander Dieleman Heiga Zen Karen Simonyan Oriol Vinyals Alex Graves Nal Kalchbrenner Andrew Senior and Koray Kavukcuoglu. 2016. Wavenet: A generative model for raw audio. In 9th ISCA Speech Synthesis Workshop . 125\u2013125."},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8080876"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/274"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301890"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_2_22_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Kitaev Nikita","year":"2019","unstructured":"Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. 2019. Reformer: The efficient transformer. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_23_2","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 34, 1 (2021), 22419\u201322430.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414142"},{"key":"e_1_3_2_25_2","volume-title":"Proceedings of the 2nd International Conference on Waste Management, Water Pollution, Air Pollution, Indoor Climate, Corfu, Greece","author":"Abdel-Rahman Adel A.","year":"2008","unstructured":"Adel A. Abdel-Rahman. 2008. On the atmospheric dispersion and Gaussian plume model. In Proceedings of the 2nd International Conference on Waste Management, Water Pollution, Air Pollution, Indoor Climate, Corfu, Greece."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0450(1992)031<0633:CADMFS>2.0.CO;2"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1175\/JAM2227.1"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.5194\/acp-5-2461-2005"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2012.12.022"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/603"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219822"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3274895.3274907"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441731"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16529"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2954510"},{"issue":"3","key":"e_1_3_2_36_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2629592","article-title":"Urban computing: Concepts, methodologies, and applications","volume":"5","author":"Zheng Yu","year":"2014","unstructured":"Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology5, 3 (2014), 1\u201355.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"e_1_3_2_37_2","first-page":"1","article-title":"Causal inference for time series analysis: Problems, methods and evaluation","author":"Moraffah Raha","year":"2021","unstructured":"Raha Moraffah, Paras Sheth, Mansooreh Karami, Anchit Bhattacharya, Qianru Wang, Anique Tahir, Adrienne Raglin, and Huan Liu. 2021. Causal inference for time series analysis: Problems, methods and evaluation. Knowledge and Information Systems 63, 12 (2021), 1\u201345.","journal-title":"Knowledge and Information Systems"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380186"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011020"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3439346"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3185010"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3526087"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2018.2868933"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3536427"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/514"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403046"},{"key":"e_1_3_2_50_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Yu Fisher","year":"2016","unstructured":"Fisher Yu and Vladlen Koltun. 2016. Multi-scale context aggregation by dilated convolutions. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_51_2","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"Shi Xingjian","year":"2015","unstructured":"Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems 28, 1 (2015), 802\u2013810.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2018.03.002"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2935152"},{"key":"e_1_3_2_54_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Li Yaguang","year":"2018","unstructured":"Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.5555\/3367243.3367303"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1002\/qj.49707331704"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1002\/qj.49707331715"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1201\/9780138733704"},{"issue":"2","key":"e_1_3_2_60_2","first-page":"265","article-title":"ARIMA models and the Box\u2013Jenkins methodology","volume":"2","author":"Asteriou Dimitros","year":"2011","unstructured":"Dimitros Asteriou and Stephen G. Hall. 2011. ARIMA models and the Box\u2013Jenkins methodology. Applied Econometrics 2, 2 (2011), 265\u2013286.","journal-title":"Applied Econometrics"},{"issue":"2","key":"e_1_3_2_61_2","first-page":"763","article-title":"Very-short-term probabilistic wind power forecasts by sparse vector autoregression","volume":"7","author":"Dowell Jethro","year":"2015","unstructured":"Jethro Dowell and Pierre Pinson. 2015. Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Transactions on Smart Grid 7, 2 (2015), 763\u2013770.","journal-title":"IEEE Transactions on Smart Grid"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604616","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3604616","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:04Z","timestamp":1750178764000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3604616"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,10]]},"references-count":61,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,11,30]]}},"alternative-id":["10.1145\/3604616"],"URL":"https:\/\/doi.org\/10.1145\/3604616","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,10]]},"assertion":[{"value":"2022-11-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-08","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}