@InProceedings{lolipiccolomini_et_al:LIPIcs.TIME.2019.10,
  author =	{Loli Piccolomini, Elena and Gandolfi, Stefano and Poluzzi, Luca and Tavasci, Luca and Cascarano, Pasquale and Pascucci, Andrea},
  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 =	{Gamper, Johann and Pinchinat, Sophie and Sciavicco, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2019.10},
  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}
}