| Title |
Gramian Angular and Markov Transition Fields Applied to Time Series Ordinal Classification |
| Authors |
Vargas V.M. , Ayllón-Gavilán R. , DURAN ROSAL, ANTONIO MANUEL, Gutiérrez P.A. , Hervás-Martínez C. , Guijo-Rubio D. |
| External publication |
No |
| Means |
Lecture Notes in Computer Science |
| Scope |
Conference Paper |
| Nature |
Científica |
| JCR Quartile |
4 |
| SJR Quartile |
2 |
| SJR Impact |
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 |
| Publication date |
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. |
| Keywords |
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 |
| Universidad Loyola members |
|
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