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Convolutional and Deep Learning based techniques for Time Series Ordinal Classification by Ayllón-Gavilán, Rafael; Guijo-Rubio, David; Gutiérrez, Pedro Antonio; Bagnall, Anthony; Hervás-Martínez, César is a scholarly article available to read on EtoBox.

What is Convolutional and Deep Learning based techniques for Time Series Ordinal Classification about?

Time Series Classification (TSC) covers the supervised learning problem where input data is provided in the form of series of values observed through repeated measurements over time, and whose objective is to predict the category to which they belong. When the class values are ordinal, classifiers that take this into account can perform better than nominal classifiers. Time Series Ordinal Classification (TSOC) is the field covering this gap, yet unexplored in the literature. There are a wide range of time series problems showing an ordered label structure, and TSC techniques that ignore the order relationship discard useful information. Hence, this paper presents a first benchmarking of TSOC methodologies, exploiting the ordering of the target labels to boost the performance of current TSC state-of-the-art. Both convolutional- and deep learning-based methodologies (among the best performing alternatives for nominal TSC) are adapted for TSOC. For the experiments, a selection of 29 ordinal problems from two well-known archives has been made. In this way, this paper contributes to the establishment of the state-of-the-art in TSOC. The results obtained by ordinal versions are found to

Author

Ayllón-Gavilán, Rafael; Guijo-Rubio, David; Gutiérrez, Pedro Antonio; Bagnall, Anthony; Hervás-Martínez, César

Published

2023

Language

EN