Abstract
Time series classification has emerged as a pivotal endeavor in the realm of machine learning applications. This task is considered supervised learning, aimed at categorizing distinct classes within time series data. The present study introduces MuRBE (Multiple Representation-Based Ensembles), an innovative meta ensemble structure explicitly designed for time series classification. The MuRBE leverages the power of diverse representation domains, including feature-based, dictionary-based, interval-based, and shapelet-based methods. Exploiting complementary information from different representations makes it particularly effective to improve classification performance. A total of thirty distinguished benchmark datasets were utilized to evaluate the effectiveness of the proposed method, leading to competitive performance results. Notably, our approach secures a second rank among current state-of-the-art techniques.
Paper Type
Short Paper
DOI
10.62036/ISD.2025.79
Time Series Classification with MuRBE: The Multiple Representation-Based Ensembles
Time series classification has emerged as a pivotal endeavor in the realm of machine learning applications. This task is considered supervised learning, aimed at categorizing distinct classes within time series data. The present study introduces MuRBE (Multiple Representation-Based Ensembles), an innovative meta ensemble structure explicitly designed for time series classification. The MuRBE leverages the power of diverse representation domains, including feature-based, dictionary-based, interval-based, and shapelet-based methods. Exploiting complementary information from different representations makes it particularly effective to improve classification performance. A total of thirty distinguished benchmark datasets were utilized to evaluate the effectiveness of the proposed method, leading to competitive performance results. Notably, our approach secures a second rank among current state-of-the-art techniques.
Recommended Citation
Sumara, R., Homenda, W., Pedrycz, W. & Yu, F. (2025). Time Series Classification with MuRBE: The Multiple Representation-Based EnsemblesIn 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.2025.79