{"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-hybrid-method-of-exponential-smoothing-and","title":"A hybrid method of Exponential Smoothing and Recurrent Neural Networks for time series forecasting","arxiv_id":null,"date":"2019-07-18","proceeding":"International Journal of Forecasting 2019 7","authors":["Slawek Smyl"],"abstract":"This paper presents the winning submission of the M4 forecasting competition. The submission utilizes a Dynamic Computational Graph Neural Network system that enables mixing of a standard Exponential Smoothing model with advanced Long Short Term Memory networks into a common framework. The result is a hybrid and hierarchical forecasting method.\r\nKeywords: Forecasting competitions, M4, Dynamic Computational Graphs, Automatic Differentiation, Long Short Term Memory (LSTM) networks, Exponential Smoothing","url_abs":"https://www.sciencedirect.com/science/article/pii/S0169207019301153","url_pdf":"https://github.com/slaweks17/ES_RNN/blob/master/A_hybrid_method_of_exponential_smoothing_and_recurrent_neural_networks_for_time_series_forecasting.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-hybrid-method-of-exponential-smoothing-and","repo_url":"https://github.com/kdgutier/esrnn_torch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"a-hybrid-method-of-exponential-smoothing-and","repo_url":"https://github.com/slaweks17/ES_RNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}