Título Gramian Angular and Markov Transition Fields Applied to Time Series Ordinal Classification
Autores Vargas V.M. , Ayllón-Gavilán R. , DURAN ROSAL, ANTONIO MANUEL, Gutiérrez P.A. , Hervás-Martínez C. , Guijo-Rubio D.
Publicación externa No
Medio Lecture Notes in Computer Science
Alcance Conference Paper
Naturaleza Científica
Cuartil JCR 4
Cuartil SJR 2
Impacto SJR 0.606
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174519770&doi=10.1007%2f978-3-031-43078-7_41&partnerID=40&md5=7687598e3a3665c11ce5e601632d722d
Fecha de publicacion 01/01/2023
Scopus Id 2-s2.0-85174519770
DOI 10.1007/978-3-031-43078-7_41
Abstract This work presents a novel ordinal Deep Learning (DL) approach to Time Series Ordinal Classification (TSOC) field. TSOC consists in classifying time series with labels showing a natural order between them. This particular property of the output variable should be exploited to boost the performance for a given problem. This paper presents a novel DL approach in which time series are encoded as 3-channels images using Gramian Angular Field and Markov Transition Field. A soft labelling approach, which considers the probabilities generated by a unimodal distribution for obtaining soft labels that replace crisp labels in the loss function, is applied to a ResNet18 model. Specifically, beta and triangular distributions have been applied. They have been compared against three state-of-the-art deep learners in the Time Series Classification (TSC) field using 13 univariate and multivariate time series datasets. The approach considering the triangular distribution (O-GAMTF T) outperforms all the techniques benchmarked. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Palabras clave Classification (of information); Deep learning; Probability distributions; Angular field; Gramian angular field; Gramians; Labelings; Markov transition field; Ordinal classification; Soft labeling; Time series ordinal classification; Times series; Transition fields; Time series
Miembros de la Universidad Loyola

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