{"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/n-beats-neural-basis-expansion-analysis-for","title":"N-BEATS: Neural basis expansion analysis for interpretable time series forecasting","arxiv_id":"1905.10437","date":"2019-05-24","proceeding":"ICLR 2020 1","authors":["Boris N. Oreshkin","Dmitri Carpov","Nicolas Chapados","Yoshua Bengio"],"abstract":"We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. 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