{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-time-series-is-worth-64-words-long-term","title":"A Time Series is Worth 64 Words: Long-term Forecasting with Transformers","arxiv_id":"2211.14730","date":"2022-11-27","proceeding":null,"authors":["Yuqi Nie","Nam H. Nguyen","Phanwadee Sinthong","Jayant Kalagnanam"],"abstract":"We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. 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