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\(_\text {T}\)) outperforms all the techniques benchmarked.
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Acknowledgements
This work has been partially subsidised by “Agencia Española de Investigación (España)” (grant ref.: PID2020-115454GB-C22 / AEI / 10.13039 / 501100011033). Víctor Manuel Vargas’s research has been subsidised by the FPU Predoctoral Program of the Spanish Ministry of Science, Innovation and Universities (MCIU), grant reference FPU18/00358. David Guijo-Rubio’s research has been subsidised by the University of Córdoba through grants to Public Universities for the requalification of the Spanish university system of the Ministry of Universities, financed by the European Union - NextGenerationEU (grant reference: UCOR01MS).
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Vargas, V.M., Ayllón-Gavilán, R., Durán-Rosal, A.M., Gutiérrez, P.A., Hervás-Martínez, C., Guijo-Rubio, D. (2023). Gramian Angular and Markov Transition Fields Applied to Time Series Ordinal Classification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. Springer, Cham. https://doi.org/10.1007/978-3-031-43078-7_41
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