{"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/ionospheric-activity-prediction-using","title":"Ionospheric activity prediction using convolutional recurrent neural networks","arxiv_id":"1810.13273","date":"2018-10-31","proceeding":null,"authors":["Alexandre Boulch","Noëlie Cherrier","Thibaut Castaings"],"abstract":"The ionosphere electromagnetic activity is a major factor of the quality of\nsatellite telecommunications, Global Navigation Satellite Systems (GNSS) and\nother vital space applications. Being able to forecast globally the Total\nElectron Content (TEC) would enable a better anticipation of potential\nperformance degradations. A few studies have proposed models able to predict\nthe TEC locally, but not worldwide for most of them. Thanks to a large record\nof past TEC maps publicly available, we propose a method based on Deep Neural\nNetworks (DNN) to forecast a sequence of global TEC maps consecutive to an\ninput sequence of TEC maps, without introducing any prior knowledge other than\nEarth rotation periodicity. By combining several state-of-the-art\narchitectures, the proposed approach is competitive with previous works on TEC\nforecasting while predicting the TEC globally.","url_abs":"http://arxiv.org/abs/1810.13273v2","url_pdf":"http://arxiv.org/pdf/1810.13273v2.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":"ionospheric-activity-prediction-using","repo_url":"https://github.com/aboulch/tec_prediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"activity-prediction","task_name":"Activity Prediction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}