{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T15:07:41Z","timestamp":1786201661502,"version":"3.56.0"},"reference-count":32,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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":["IEEE Trans. Knowl. Data Eng."],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1109\/tkde.2023.3342137","type":"journal-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T19:53:01Z","timestamp":1702497181000},"page":"6851-6864","source":"Crossref","is-referenced-by-count":233,"title":["PromptCast: A New Prompt-Based Learning Paradigm for Time Series Forecasting"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1700-9215","authenticated-orcid":false,"given":"Hao","family":"Xue","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1237-1664","authenticated-orcid":false,"given":"Flora D.","family":"Salim","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.113"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref4","article-title":"On the opportunities and risks of foundation models","author":"Bommasani","year":"2021"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref6","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Radford"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01069"},{"key":"ref8","article-title":"Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref10","first-page":"1","article-title":"Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref11","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Xu"},{"key":"ref12","first-page":"27 268","article-title":"FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhou"},{"key":"ref13","first-page":"5447","article-title":"TACTiS: Transformer-attentional copulas for time series","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Drouin"},{"key":"ref14","article-title":"A generalist agent","author":"Reed","year":"2022"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498387"},{"key":"ref16","first-page":"140:1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"ref18","article-title":"RoBERTa: A robustly optimized BERT pretraining approach","author":"Liu","year":"2019"},{"key":"ref19","article-title":"ELECTRA: Pre-training text encoders as discriminators rather than generators","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Clark"},{"key":"ref20","first-page":"17 283","article-title":"Big bird: Transformers for longer sequences","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zaheer"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.217"},{"key":"ref22","article-title":"Longformer: The long-document transformer","author":"Beltagy","year":"2020"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.24"},{"key":"ref24","first-page":"11 328","article-title":"PEGASUS: Pre-training with extracted gap-sentences for abstractive summarization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.11"},{"issue":"8","key":"ref27","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"ref28","first-page":"30 380","article-title":"MobTCast: Leveraging auxiliary trajectory forecasting for human mobility prediction","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Xue"},{"key":"ref29","first-page":"11135","article-title":"Image captioning: Transforming objects into words","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Herdade"},{"key":"ref30","article-title":"LIFT: Language-interfaced fine-tuning for non-language machine learning tasks","author":"Dinh","year":"2022"},{"key":"ref31","first-page":"1","article-title":"A dataset for answering time-sensitive questions","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst. Track Datasets Benchmarks","author":"Chen"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.357"}],"container-title":["IEEE Transactions on Knowledge and Data Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/69\/10709365\/10356715.pdf?arnumber=10356715","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T05:38:07Z","timestamp":1728452287000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10356715\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":32,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tkde.2023.3342137","relation":{},"ISSN":["1041-4347","1558-2191","2326-3865"],"issn-type":[{"value":"1041-4347","type":"print"},{"value":"1558-2191","type":"electronic"},{"value":"2326-3865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11]]}}}