{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T02:12:06Z","timestamp":1765505526672,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","funder":[{"name":"EPFL","award":["Solutions for Sustainability Initiative (S4S)"],"award-info":[{"award-number":["Solutions for Sustainability Initiative (S4S)"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3760905","type":"proceedings-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:36:36Z","timestamp":1762562196000},"page":"5058-5062","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Solar Forecasting with Causality: A Graph-Transformer Approach to Spatiotemporal Dependencies"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0589-9724","authenticated-orcid":false,"given":"Yanan","family":"Niu","sequence":"first","affiliation":[{"name":"EPFL, Lausanne, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4684-8800","authenticated-orcid":false,"given":"Demetri","family":"Psaltis","sequence":"additional","affiliation":[{"name":"EPFL, Lausanne, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2078-0273","authenticated-orcid":false,"given":"Christophe","family":"Moser","sequence":"additional","affiliation":[{"name":"EPFL, Lausanne, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6425-4606","authenticated-orcid":false,"given":"Luisa","family":"Lambertini","sequence":"additional","affiliation":[{"name":"EPFL, Lausanne, Switzerland and Universit\u00e0 della Svizzera italiana (USI), Lugano, Switzerland"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"IEA PVPS TASK 1. 2025. Global Market Outlook for Solar Power 2025-2029. Technical Report. The International Energy Agency."},{"key":"e_1_3_2_1_2_1","first-page":"55375","article-title":"Taming local effects in graph-based spatiotemporal forecasting","volume":"36","author":"Cini Andrea","year":"2023","unstructured":"Andrea Cini, Ivan Marisca, Daniele Zambon, and Cesare Alippi. 2023. Taming local effects in graph-based spatiotemporal forecasting. Advances in Neural Information Processing Systems, Vol. 36 (2023), 55375-55393.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_3_1","volume-title":"International conference on machine learning. PMLR, 933-941","author":"Dauphin Yann N","year":"2017","unstructured":"Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier. 2017. Language modeling with gated convolutional networks. In International conference on machine learning. PMLR, 933-941."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00313"},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Machine Learning. PMLR, 7052-7076","author":"Gao Jianfei","year":"2022","unstructured":"Jianfei Gao and Bruno Ribeiro. 2022. On the equivalence between temporal and static equivariant graph representations. In International Conference on Machine Learning. PMLR, 7052-7076."},{"key":"e_1_3_2_1_6_1","volume-title":"International conference on machine learning. PMLR, 1243-1252","author":"Gehring Jonas","year":"2017","unstructured":"Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017. Convolutional sequence to sequence learning. In International conference on machine learning. PMLR, 1243-1252."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3056502"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709201"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102233"},{"key":"e_1_3_2_1_11_1","volume-title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations (ICLR '18)","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 International Conference on Learning Representations (ICLR '18)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV61041.2025.00494"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.5555\/1642718"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2012.04.004"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557702"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.117514"},{"key":"e_1_3_2_1_17_1","unstructured":"Aaron van den Oord Sander Dieleman Heiga Zen Karen Simonyan Oriol Vinyals Alex Graves Nal Kalchbrenner Andrew Senior and Koray Kavukcuoglu. 2016a. WaveNet: A Generative Model for Raw Audio. arXiv:1609.03499 [cs.SD] https:\/\/arxiv.org\/abs\/1609.03499"},{"key":"e_1_3_2_1_18_1","volume-title":"Garnett (Eds.)","volume":"29","author":"van den Oord Aaron","year":"2016","unstructured":"Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, koray kavukcuoglu, Oriol Vinyals, and Alex Graves. 2016b. Conditional Image Generation with PixelCNN Decoders. In Advances in Neural Information Processing Systems, D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Eds.), Vol. 29. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2016\/file\/b1301141feffabac455e1f90a7de2054-Paper.pdf"},{"key":"e_1_3_2_1_19_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. arXiv:2005.11650 [cs.LG] https:\/\/arxiv.org\/abs\/2005.11650"},{"key":"e_1_3_2_1_20_1","first-page":"37068","article-title":"Deciphering spatio-temporal graph forecasting: A causal lens and treatment","volume":"36","author":"Xia Yutong","year":"2023","unstructured":"Yutong Xia, Yuxuan Liang, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, and Roger Zimmermann. 2023. Deciphering spatio-temporal graph forecasting: A causal lens and treatment. Advances in Neural Information Processing Systems, Vol. 36 (2023), 37068-37088.","journal-title":"Advances in Neural Information Processing Systems"},{"volume-title":"International Conference on Complex Networks and Their Applications","author":"Xu Nancy","key":"e_1_3_2_1_21_1","unstructured":"Nancy Xu, Chrysoula Kosma, and Michalis Vazirgiannis. 2023. TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting. In International Conference on Complex Networks and Their Applications. Springer, 87-99."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","unstructured":"Ailing Zeng Muxi Chen Lei Zhang and Qiang Xu. 2023. Are transformers effective for time series forecasting?. In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence (AAAI'23\/IAAI'23\/EAAI'23). AAAI Press Article 1248 8 pages. doi:10.1609\/aaai.v37i9.26317","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"e_1_3_2_1_25_1","volume-title":"T-GCN: A temporal graph convolutional network for traffic prediction","author":"Zhao Ling","year":"2019","unstructured":"Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. 2019. T-GCN: A temporal graph convolutional network for traffic prediction. IEEE transactions on intelligent transportation systems, Vol. 21, 9 (2019), 3848-3858."}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Seoul Republic of Korea","acronym":"CIKM '25"},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3760905","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T02:09:18Z","timestamp":1765505358000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3760905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":25,"alternative-id":["10.1145\/3746252.3760905","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3760905","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}