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@InProceedings{lolipiccolomini_et_al:LIPIcs:2019:11368, author = {Elena Loli Piccolomini and Stefano Gandolfi and Luca Poluzzi and Luca Tavasci and Pasquale Cascarano and Andrea Pascucci}, title = {{Recurrent Neural Networks Applied to GNSS Time Series for Denoising and Prediction}}, booktitle = {26th International Symposium on Temporal Representation and Reasoning (TIME 2019)}, pages = {10:1--10:12}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-127-6}, ISSN = {1868-8969}, year = {2019}, volume = {147}, editor = {Johann Gamper and Sophie Pinchinat and Guido Sciavicco}, publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik}, address = {Dagstuhl, Germany}, URL = {http://drops.dagstuhl.de/opus/volltexte/2019/11368}, URN = {urn:nbn:de:0030-drops-113687}, doi = {10.4230/LIPIcs.TIME.2019.10}, annote = {Keywords: Deep Neural Networks, Recurrent Neural Networks, Time Series Denoising, Time Series Prediction} }
Keywords: | Deep Neural Networks, Recurrent Neural Networks, Time Series Denoising, Time Series Prediction | |
Seminar: | 26th International Symposium on Temporal Representation and Reasoning (TIME 2019) | |
Issue date: | 2019 | |
Date of publication: | 07.10.2019 |