{"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/battling-the-non-stationarity-in-time-series","title":"Battling the Non-stationarity in Time Series Forecasting via Test-time Adaptation","arxiv_id":"2501.04970","date":"2025-01-09","proceeding":null,"authors":["HyunGi Kim","Siwon Kim","Jisoo Mok","Sungroh Yoon"],"abstract":"Deep Neural Networks have spearheaded remarkable advancements in time series forecasting (TSF), one of the major tasks in time series modeling. 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