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Temporal Association Rules from Motifs (Short Paper)

Authors Mauro Milella , Giovanni Pagliarini , Guido Sciavicco , Ionel Eduard Stan

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Subject Classification

ACM Subject Classification
  • Theory of computation → Modal and temporal logics
  • Theory of computation → Theory and algorithms for application domains
Keywords
  • Motifs
  • Interval Temporal Logic
  • Association Rules

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Abstract

A motif is defined as a frequently occurring pattern within a (multivariate) time series. In recent years, various techniques have been developed to mine time series data. However, only a few studies have explored the idea of using motif discovery in temporal association rule mining. Interval-based temporal association rules have been recently defined and studied, along with the temporal version of the known frequent patterns, and therefore, association rule extraction algorithms (such as APRIORI and FP-Growth). In this work, we define a vocabulary of propositional letters wrapping motifs, and show how to extract temporal association rules starting from such a vocabulary. We apply our methodology to time series datasets in the fields of hand signs execution and gait recognition, and we discuss how they capture curious insights within data, keeping a high level of interpretability.

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Mauro Milella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan. Temporal Association Rules from Motifs (Short Paper). In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 19:1-19:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.TIME.2025.19

Author Details

Mauro Milella
  • Department of Mathematics and Computer Science, University of Ferrara, Italy
Giovanni Pagliarini
  • Department of Mathematics and Computer Science, University of Ferrara, Italy
Guido Sciavicco
  • Department of Mathematics and Computer Science, University of Ferrara, Italy
Ionel Eduard Stan
  • Department of Informatics, Systems, and Communications, University of Milano-Bicocca, Italy

Funding

We acknowledge the support of the FIRD project Methodological Developments in Modal Symbolic Geometric Learning, funded by the University of Ferrara. Moreover, this research has also been funded by the Italian Ministry of University and Research through PNRR - M4C2 - Investimento 1.3 (Decreto Direttoriale MUR n. 341 del 15/03/2022), Partenariato Esteso PE00000013 - "FAIR - Future Artificial Intelligence Research" - Spoke 8 "Pervasive AI", funded by the European Union under the NextGeneration EU programme.

Supplementary Materials

References

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    Software Heritage Logo archived version
    full metadata available at: https://doi.org/10.4230/artifacts.24783
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