{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:39:51Z","timestamp":1723016391834},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"Spatiotemporal data aggregated over regions or time windows at various resolutions demonstrate heterogeneous patterns and dynamics in each resolution. Meanwhile, the multi-resolution characteristic provides rich contextual information, which is critical for effective long-sequence forecasting. The importance of such inter-resolution information is more significant in practical cases, where fine-grained data is usually collected via approaches with lower costs but also lower qualities compared to those for coarse-grained data. However, existing works focus on uni-resolution data and cannot be directly applied to fully utilize the aforementioned extra information in multi-resolution data. In this work, we propose Spatiotemporal Koopman Multi-Resolution Network (ST-KMRN), a physics-informed learning framework for long-sequence forecasting from multi-resolution spatiotemporal data. Our method jointly models data aggregated in multiple resolutions and captures the inter-resolution dynamics with the self-attention mechanism. We also propose downsampling and upsampling modules among resolutions to further strengthen the connections among data of multiple resolutions. Moreover, we enhance the modeling of intra-resolution dynamics with physics-informed modules based on Koopman theory. Experimental results demonstrate that our proposed approach achieves the best performance on the long-sequence forecasting tasks compared to baselines without a specific design for multi-resolution data.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/304","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"2189-2195","source":"Crossref","is-referenced-by-count":2,"title":["Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data"],"prefix":"10.24963","author":[{"given":"Chuizheng","family":"Meng","sequence":"first","affiliation":[{"name":"University of Southern California"}]},{"given":"Hao","family":"Niu","sequence":"additional","affiliation":[{"name":"KDDI Research, Inc."}]},{"given":"Guillaume","family":"Habault","sequence":"additional","affiliation":[{"name":"KDDI Research, Inc."}]},{"given":"Roberto","family":"Legaspi","sequence":"additional","affiliation":[{"name":"KDDI Research, Inc."}]},{"given":"Shinya","family":"Wada","sequence":"additional","affiliation":[{"name":"KDDI Research, Inc."}]},{"given":"Chihiro","family":"Ono","sequence":"additional","affiliation":[{"name":"KDDI Research, Inc."}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Southern California"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:08:56Z","timestamp":1658142536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/304"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/304","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}