{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T12:16:54Z","timestamp":1775564214866,"version":"3.50.1"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,4,14]]},"DOI":"10.1109\/icassp48485.2024.10448031","type":"proceedings-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T18:56:31Z","timestamp":1710788191000},"page":"5735-5739","source":"Crossref","is-referenced-by-count":6,"title":["Multivariate Time Series Forecasting with Causal-Temporal Attention Network"],"prefix":"10.1109","author":[{"given":"Wenbo","family":"Liu","sequence":"first","affiliation":[{"name":"Fudan University,School of Computer Science,Shanghai,China,200438"}]},{"given":"Yifan","family":"He","sequence":"additional","affiliation":[{"name":"Fudan University,School of Computer Science,Shanghai,China,200438"}]},{"given":"Jihong","family":"Guan","sequence":"additional","affiliation":[{"name":"Tongji University,Department of Computer Science and Technology,Shanghai,China,201804"}]},{"given":"Shuigeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Fudan University,School of Computer Science,Shanghai,China,200438"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.123990"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108218"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.123403"},{"key":"ref4","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume":"32","author":"Li","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.12.118"},{"key":"ref6","article-title":"Reformer: The efficient transformer","author":"Kitaev","year":"2020"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref8","article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","volume-title":"The Eleventh International Conference on Learning Representations","author":"Zhang"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i6.25854"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210006"},{"key":"ref11","article-title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting","volume-title":"International conference on learning representations","author":"Liu"},{"key":"ref12","article-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"Li","year":"2017"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"ref15","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220104"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.21437\/interspeech.2014-80"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref19","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref20","first-page":"27268","article-title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"International Conference on Machine Learning","author":"Zhou"}],"event":{"name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Seoul, Korea, Republic of","start":{"date-parts":[[2024,4,14]]},"end":{"date-parts":[[2024,4,19]]}},"container-title":["ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10445798\/10445803\/10448031.pdf?arnumber=10448031","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,2]],"date-time":"2024-08-02T06:38:45Z","timestamp":1722580725000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10448031\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,14]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/icassp48485.2024.10448031","relation":{},"subject":[],"published":{"date-parts":[[2024,4,14]]}}}