Gramian Angular and Markov Transition Fields applied to Time Series Ordinal Classification
Congress: IWANN 2023: Advances in Computational Intelligence
City: Ponta Delgada (Portugal)
Date: 19th-21th June
Year: 2023
Pages: 505 - 516
URL: https://link.springer.com/chapter/10.1007/978-3-031-43078-7_41Abstract
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) outperforms all the techniques benchmarked.
Citation
@inproceedings{vargas2023gramian,
title={Gramian Angular and Markov Transition Fields Applied to Time Series Ordinal Classification},
author={Vargas, V{\'\i}ctor Manuel and Ayll{\'o}n-Gavil{\'a}n, Rafael and Dur{\'a}n-Rosal, Antonio Manuel and Guti{\'e}rrez, Pedro Antonio and Herv{\'a}s-Mart{\'\i}nez, C{\'e}sar and Guijo-Rubio, David},
booktitle={International Work-Conference on Artificial Neural Networks},
pages={505--516},
year={2023},
organization={Springer}
}