Convolutional and Deep Learning based techniques for Time Series Ordinal Classification

Research areas:

• Uncategorized

Year:

2024

Type of Publication:

Article

Keywords:

time series machine learning, time series analysis, time series classification, ordinal classification

Authors:

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

Journal:

IEEE Transactions on Cybernetics

Volume:

Accepted on 12th November

ISSN:

2168-2267

BibTex:

@article{RAFACyb2024, author = "Rafael Ayll{\'o}n-Gavil{\'a}n and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Anthony Bagnall and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "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 bridging 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 the 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 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 be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems.", awards = "JCR(2023): 9.4, Position: 16/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2023): 9.4, Position: 16/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", issn = "2168-2267", journal = "IEEE Transactions on Cybernetics", keywords = "time series machine learning, time series analysis, time series classification, ordinal classification", note = "JCR(2023): 9.4, Position: 16/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", title = "{C}onvolutional and {D}eep {L}earning based techniques for {T}ime {S}eries {O}rdinal {C}lassification", volume = "Accepted on 12th November", year = "2024", }

Note:

JCR(2023): 9.4, Position: 16/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Abstract:

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 bridging 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 the 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 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 be significantly better than current nominal TSC techniques in terms of ordinal performance metrics, outlining the importance of considering the ordering of the labels when dealing with this kind of problems.

Comments:

JCR(2023): 9.4, Position: 16/197 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE