@conference{GramarianAngular_iwan_2023,
author = "V{\'i}ctor Manuel Vargas and Rafael Ayll{\'o}n-Gavil{\'a}n and Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and David Guijo-Rubio",
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-GAMTFT) outperforms all the techniques benchmarked.",
booktitle = "IWANN 2023: Advances in Computational Intelligence",
doi = "10.1007/978-3-031-43078-7_41",
keywords = "Gramarian Angular Fields, Markov Transition Fields, Time Series Ordinal Classification, Soft Labelling",
number = "1",
pages = "1--12",
title = "{G}ramian {A}ngular and {M}arkov {T}ransition {F}ields applied to {T}ime {S}eries {O}rdinal {C}lassification",
url = "link.springer.com/chapter/10.1007/978-3-031-43078-7_41",
year = "2023",
}