{"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-neural-network-ensembles-for-time-series","title":"Deep Neural Network Ensembles for Time Series Classification","arxiv_id":"1903.06602","date":"2019-03-15","proceeding":null,"authors":["Hassan Ismail Fawaz","Germain Forestier","Jonathan Weber","Lhassane Idoumghar","Pierre-Alain Muller"],"abstract":"Deep neural networks have revolutionized many fields such as computer vision\nand natural language processing. Inspired by this recent success, deep learning\nstarted to show promising results for Time Series Classification (TSC).\nHowever, neural networks are still behind the state-of-the-art TSC algorithms,\nthat are currently composed of ensembles of 37 non deep learning based\nclassifiers. We attribute this gap in performance due to the lack of neural\nnetwork ensembles for TSC. Therefore in this paper, we show how an ensemble of\n60 deep learning models can significantly improve upon the current\nstate-of-the-art performance of neural networks for TSC, when evaluated over\nthe UCR/UEA archive: the largest publicly available benchmark for time series\nanalysis. Finally, we show how our proposed Neural Network Ensemble (NNE) is\nthe first time series classifier to outperform COTE while reaching similar\nperformance to the current state-of-the-art ensemble HIVE-COTE.","url_abs":"http://arxiv.org/abs/1903.06602v2","url_pdf":"http://arxiv.org/pdf/1903.06602v2.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-neural-network-ensembles-for-time-series","repo_url":"https://github.com/hfawaz/ijcnn19ensemble","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"deep-neural-network-ensembles-for-time-series","repo_url":"https://github.com/hfawaz/cd-diagram","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"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":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.06602","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}