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2023 | OriginalPaper | Chapter

Semi-supervised Time Series Classification Through Image Representations

Authors : Bionda Rozin, Emílio Bergamim, Daniel Carlos Guimarães Pedronette, Fabricio Aparecido Breve

Published in: Computational Science and Its Applications – ICCSA 2023

Publisher: Springer Nature Switzerland

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Abstract

Time series data is of crucial importance in different domains, such as financial and medical applications. However, obtaining a large amount of labeled time series data is an expensive and time-consuming task, which becomes the process of building an effective machine learning model a challenge. In these scenarios, algorithms that can deal with reduced amounts of labeled data emerge. One example is Semi-Supervised Learning (SSL), which has the capability of exploring both labeled and unlabeled data for tasks such as classification. In this work, a kNN graph-based transductive SSL approach is used for time series classification. A feature extraction step, based on imaging time series and obtaining features using deep neural networks is performed before the classification step. An extensive evaluation is conducted over four datasets, and a parametric analysis of the nearest neighbors is performed. Also, a statistical analysis over the obtained distances is conducted. Results suggest that our methods are suitable for classification and competitive with supervised baselines in some datasets.

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Title: Semi-supervised Time Series Classification Through Image Representations
Authors: Bionda Rozin
Emílio Bergamim
Daniel Carlos Guimarães Pedronette
Fabricio Aparecido Breve
Publisher: Springer Nature Switzerland
Book: Computational Science and Its Applications – ICCSA 2023
Print ISBN: 978-3-031-36807-3

Electronic ISBN: 978-3-031-36808-0

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-36808-0_4

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