Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification

Edgar Acuña () and Roxana Aparicio
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Edgar Acuña: Mathematical Science Department, University of Puerto Rico at Mayaguez, Mayaguez PR00681, Puerto Rico
Roxana Aparicio: Department of Industrial Engineering, University of Puerto Rico at Mayaguez, Mayaguez PR00681, Puerto Rico

Data, 2025, vol. 10, issue 5, 1-25

Abstract: In this paper, we use visualization tools to give insight into the performance of six classifiers on multivariate time series data. Five of these classifiers are deep learning models, while the Rocket classifier represents a non-deep learning approach. Our comparison is conducted across fifteen datasets from the UEA repository. Additionally, we apply data engineering techniques to each dataset, allowing us to assess classifier performance concerning the available features and channels within the time series. The results of our experiments indicate that the ROCKET classifier consistently achieves strong performance across most datasets, while the Transformer model underperforms, likely due to the limited number of instances per class in certain datasets.

Keywords: multivariate time series classification; UEA archive; time series visualization; deep learning classifiers (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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