Abstract
This research paper concerns the classification of multiclass univariate time series. It extends the existing time series imaging method and describes the process of obtaining new transformations of this sort. The presented techniques leverage the well-known Symbolic Aggregate Approximation representation, which transforms time series from numerical to symbolic domain. The obtained symbolic approximations are later turned into images. After raw time series data is transformed into two-dimensional grayscale bitmaps, these bitmaps are used as input for two alternative deep learning classification approaches. Our study focuses on comparing regular Convolutional Neural Networks and Siamese Neural Networks as time series classifiers. Experimental studies and comparative analyses were performed on well-known, publicly available datasets.
Paper Type
Poster
DOI
10.62036/ISD.2025.36
Classification of SAX-like Time Series Bitmaps with Siamese Convolutional Neural Networks
This research paper concerns the classification of multiclass univariate time series. It extends the existing time series imaging method and describes the process of obtaining new transformations of this sort. The presented techniques leverage the well-known Symbolic Aggregate Approximation representation, which transforms time series from numerical to symbolic domain. The obtained symbolic approximations are later turned into images. After raw time series data is transformed into two-dimensional grayscale bitmaps, these bitmaps are used as input for two alternative deep learning classification approaches. Our study focuses on comparing regular Convolutional Neural Networks and Siamese Neural Networks as time series classifiers. Experimental studies and comparative analyses were performed on well-known, publicly available datasets.
Recommended Citation
Wrzesień, M., Wrzesień, M. & Korniak, J. (2025). Classification of SAX-like Time Series Bitmaps with Siamese Convolutional Neural NetworksIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.36