License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.TIME.2023.1
URN: urn:nbn:de:0030-drops-190917
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/19091/
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Nenzi, Laura

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

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LIPIcs-TIME-2023-1.pdf (0.4 MB)

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.

BibTeX - Entry

@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/opus/volltexte/2023/19091},
  URN =		{urn:nbn:de:0030-drops-190917},
  doi =		{10.4230/LIPIcs.TIME.2023.1},
  annote =	{Keywords: Temporal Logic, Mining Specifications}
}

Keywords: Temporal Logic, Mining Specifications
Collection: 30th International Symposium on Temporal Representation and Reasoning (TIME 2023)
Issue Date: 2023
Date of publication: 18.09.2023

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