{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:04:45Z","timestamp":1750309485222,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T00:00:00Z","timestamp":1726272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,9,14]]},"DOI":"10.1145\/3697355.3697401","type":"proceedings-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T04:52:23Z","timestamp":1734065543000},"page":"276-281","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust Spatio-Temporal Graph Neural Network for Electricity Consumption Forecasting"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8504-8771","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"first","affiliation":[{"name":"State Grid Economic and Technological Research Institute Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-7698","authenticated-orcid":false,"given":"Fuyong","family":"Sun","sequence":"additional","affiliation":[{"name":"State Grid Economic and Technological Research Institute Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8506-4967","authenticated-orcid":false,"given":"Jinxin","family":"Si","sequence":"additional","affiliation":[{"name":"State Grid Economic and Technological Research Institute Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1760-638X","authenticated-orcid":false,"given":"Qiuzhe","family":"Ma","sequence":"additional","affiliation":[{"name":"State Grid Economic and Technological Research Institute Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3500-6150","authenticated-orcid":false,"given":"Wenjing","family":"Zeng","sequence":"additional","affiliation":[{"name":"State Grid Economic and Technological Research Institute Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3593-4265","authenticated-orcid":false,"given":"Xiuhuan","family":"Zang","sequence":"additional","affiliation":[{"name":"State Grid Economic and Technological Research Institute Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7247-3689","authenticated-orcid":false,"given":"Junxi","family":"Cao","sequence":"additional","affiliation":[{"name":"State Grid Economic and Technological Research Institute Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-3247-9070","authenticated-orcid":false,"given":"Shuaibing","family":"Song","sequence":"additional","affiliation":[{"name":"State Grid Economic and Technological Research Institute Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6626-7783","authenticated-orcid":false,"given":"Nan","family":"Wang","sequence":"additional","affiliation":[{"name":"State Grid Economic and Technological Research Institute Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,12,12]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"Shaojie Bai J\u00a0Zico Kolter and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1803.01271 (2018)."},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/277"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467330"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Xiaochong Dong Yingyun Sun et\u00a0al. 2021. Spatio-temporal convolutional network based power forecasting of multiple wind farms. Journal of Modern Power Systems and Clean Energy 10 2 (2021) 388\u2013398.","DOI":"10.35833\/MPCE.2020.000849"},{"key":"e_1_3_3_2_6_2","first-page":"417","volume-title":"WWW","author":"Fan Wenqi","year":"2019","unstructured":"Wenqi Fan, Yao Ma, et\u00a0al. 2019. Graph neural networks for social recommendation. In WWW. 417\u2013426."},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"En Fu Yinong Zhang Fan Yang and Shuying Wang. 2022. Temporal self-attention-based Conv-LSTM network for multivariate time series prediction. Neurocomputing 501 (2022) 162\u2013173.","DOI":"10.1016\/j.neucom.2022.06.014"},{"key":"e_1_3_3_2_8_2","unstructured":"Guolin Ke Qi Meng Thomas Finley et\u00a0al. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"e_1_3_3_2_10_2","unstructured":"Aaron van\u00a0den Oord Sander Dieleman et\u00a0al. 2016. Wavenet: A generative model for raw audio. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1609.03499 (2016)."},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Carlos\u00a0A Severiano et\u00a0al. 2021. Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems. Renewable Energy 171 (2021) 764\u2013783.","DOI":"10.1016\/j.renene.2021.02.117"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Jelena et\u00a0al. Simeunovi\u0107. 2021. Spatio-temporal graph neural networks for multi-site PV power forecasting. IEEE Transactions on Sustainable Energy 13 2 (2021) 1210\u20131220.","DOI":"10.1109\/TSTE.2021.3125200"},{"key":"e_1_3_3_2_13_2","unstructured":"Fuyong Sun Ruipeng Gao Weiwei Xing et\u00a0al. 2022. Deep Fusion for Travel Time Estimation Based on Road Network Topology. IEEE Intelligent Systems (2022)."},{"key":"e_1_3_3_2_14_2","doi-asserted-by":"crossref","unstructured":"Fuyong Sun Weiwei Xing Xiaofei Tian et\u00a0al. 2023. Dual-norm based dynamic graph diffusion network for temporal prediction. Information Processing & Management 60 4 (2023) 103387.","DOI":"10.1016\/j.ipm.2023.103387"},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"AR Troncoso-Garc\u00eda et\u00a0al. 2023. A new approach based on association rules to add explainability to time series forecasting models. Information Fusion 94 (2023) 169\u2013180.","DOI":"10.1016\/j.inffus.2023.01.021"},{"key":"e_1_3_3_2_16_2","unstructured":"Ashish Vaswani Noam Shazeer et\u00a0al. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015409"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/264"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3377713.3377722"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413939"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"G\u00a0Peter Zhang. 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50 (2003) 159\u2013175.","DOI":"10.1016\/S0925-2312(01)00702-0"},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_3_2_24_2","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. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2201.12740 (2022)."},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i13.26853"},{"key":"e_1_3_3_2_26_2","unstructured":"Eric Zivot and Jiahui Wang. 2006. Vector autoregressive models for multivariate time series. Modeling financial time series with S-PLUS\u00ae (2006) 385\u2013429."}],"event":{"name":"BDIOT 2024: 2024 8th International Conference on Big Data and Internet of Things","acronym":"BDIOT 2024","location":"Macau China"},"container-title":["Proceedings of the 2024 8th International Conference on Big Data and Internet of Things"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3697355.3697401","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3697355.3697401","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:34Z","timestamp":1750295854000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3697355.3697401"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,14]]},"references-count":25,"alternative-id":["10.1145\/3697355.3697401","10.1145\/3697355"],"URL":"https:\/\/doi.org\/10.1145\/3697355.3697401","relation":{},"subject":[],"published":{"date-parts":[[2024,9,14]]},"assertion":[{"value":"2024-12-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}