Linguistic summaries for periodicity detection based on mathematical morphology

Abstract : 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.
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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⟩

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