Linguistic summaries for periodicity detection based on mathematical morphology

Gilles Moyse (1) , Marie-Jeanne Lesot (1) , Bernadette Bouchon-Meunier (1)

1 LFI - Learning, Fuzzy and Intelligent systems

The paper presents a methodology to evaluate the periodicity of a temporal data series, neither relying on assumption about the series form nor requiring expert knowledge to set parameters. It exploits tools from mathematical morphology to compute a periodicity degree and a candidate period, as well as the fuzzy set theory to generate a natural language sentence, improving the result interpretability. Experiments on both artificial and real data illustrate the relevance of the proposed approach.

Domaines: Algorithme et structure de données [cs.DS]; Apprentissage [cs.LG]; Base de données [cs.DB]; Informatique et langage [cs.CL]; Mathématique discrète [cs.DM]; Recherche d'information [cs.IR]

Gilles Moyse, Marie-Jeanne Lesot, Bernadette Bouchon-Meunier. Linguistic summaries for periodicity detection based on mathematical morphology. IEEE Symposium Series on Computational Intelligence, Apr 2013, Singapore, Singapore. pp.106-113, ⟨10.1109/FOCI.2013.6602462⟩. ⟨hal-00932852⟩