| 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 |
Lect. Notes Comput. Sci. |
| Alcance |
Conference Paper |
| Naturaleza |
Científica |
| Cuartil JCR |
4 |
| Cuartil SJR |
3 |
| Fecha de publicacion |
01/01/2023 |
| 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; Ti |
| Miembros de la Universidad Loyola |
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