Gramian Angular and Markov Transition Fields applied to Time Series Ordinal Classification
Hits: 2309
- Áreas de investigación:
- Sin categoría
- Año:
- 2023
- Tipo de publicación:
- Artículo en conferencia
- Palabras clave:
- Gramarian Angular Fields, Markov Transition Fields, Time Series Ordinal Classification, Soft Labelling
- Autores:
- Título del libro:
- IWANN 2023: Advances in Computational Intelligence
- Número:
- 1
- Páginas:
- 1-12
- BibTex:
- 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.