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Invited Talk
Learning Temporal Logic Formulas from Time-Series Data (Invited Talk)

Authors: Laura Nenzi

Published in: LIPIcs, Volume 278, 30th International Symposium on Temporal Representation and Reasoning (TIME 2023)


Abstract
In this talk, we provide an overview of recent advancements in the field of mining formal specifications from time-series data, with a specific focus on learning Signal Temporal Logic (STL) formulae.

Cite as

Laura Nenzi. Learning Temporal Logic Formulas from Time-Series Data (Invited Talk). In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 1:1-1:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{nenzi:LIPIcs.TIME.2023.1,
  author =	{Nenzi, Laura},
  title =	{{Learning Temporal Logic Formulas from Time-Series Data}},
  booktitle =	{30th International Symposium on Temporal Representation and Reasoning (TIME 2023)},
  pages =	{1:1--1:2},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-298-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{278},
  editor =	{Artikis, Alexander and Bruse, Florian and Hunsberger, Luke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2023.1},
  URN =		{urn:nbn:de:0030-drops-190917},
  doi =		{10.4230/LIPIcs.TIME.2023.1},
  annote =	{Keywords: Temporal Logic, Mining Specifications}
}
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