{"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/deep-gated-recurrent-and-convolutional","title":"Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification","arxiv_id":"1812.07683","date":"2018-12-18","proceeding":null,"authors":["Nelly Elsayed","Anthony S. Maida","Magdy Bayoumi"],"abstract":"Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series\nclassification have produced state-of-the-art classification results on\nunivariate time series. We show that replacing the LSTM with a gated recurrent\nunit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN)\ncan offer even better performance on many time series datasets. The proposed\nGRU-FCN model outperforms state-of-the-art classification performance in many\nunivariate and multivariate time series datasets. In addition, since the GRU\nuses a simpler architecture than the LSTM, it has fewer training parameters,\nless training time, and a simpler hardware implementation, compared to the\nLSTM-based models.","url_abs":"http://arxiv.org/abs/1812.07683v3","url_pdf":"http://arxiv.org/pdf/1812.07683v3.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":"deep-gated-recurrent-and-convolutional","repo_url":"https://github.com/NellyElsayed/GRU-FCN-model-for-univariate-time-series-classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.07683","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}