Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification
Year: 2025
Type: article
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 twelve 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.
Source: Data
DOI: 10.3390/data10050058
Authors: Edgar Acuña, Roxana Aparicio
Institution: University of Puerto Rico-Mayaguez
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4409768401