{"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/neural-forecasting-introduction-and","title":"Deep Learning for Time Series Forecasting: Tutorial and Literature Survey","arxiv_id":"2004.10240","date":"2020-04-21","proceeding":null,"authors":["Konstantinos Benidis","Syama Sundar Rangapuram","Valentin Flunkert","Yuyang Wang","Danielle Maddix","Caner Turkmen","Jan Gasthaus","Michael Bohlke-Schneider","David Salinas","Lorenzo Stella","Francois-Xavier Aubet","Laurent Callot","Tim Januschowski"],"abstract":"Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches. Consequently, over the last years, these methods are now ubiquitous in large-scale industrial forecasting applications and have consistently ranked among the best entries in forecasting competitions (e.g., M4 and M5). This practical success has further increased the academic interest to understand and improve deep forecasting methods. 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