Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification
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 data...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2306-5729/10/5/58 |
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| author | Edgar Acuña Roxana Aparicio |
| author_facet | Edgar Acuña Roxana Aparicio |
| author_sort | Edgar Acuña |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d7601a0aa893456d88f6f6c86c166d57 |
| institution | OA Journals |
| issn | 2306-5729 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj-art-d7601a0aa893456d88f6f6c86c166d572025-08-20T02:33:31ZengMDPI AGData2306-57292025-04-011055810.3390/data10050058Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series ClassificationEdgar Acuña0Roxana Aparicio1Mathematical Science Department, University of Puerto Rico at Mayaguez, Mayaguez PR00681, Puerto RicoDepartment of Industrial Engineering, University of Puerto Rico at Mayaguez, Mayaguez PR00681, Puerto RicoIn 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.https://www.mdpi.com/2306-5729/10/5/58multivariate time series classificationUEA archivetime series visualizationdeep learning classifiers |
| spellingShingle | Edgar Acuña Roxana Aparicio Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification Data multivariate time series classification UEA archive time series visualization deep learning classifiers |
| title | Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification |
| title_full | Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification |
| title_fullStr | Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification |
| title_full_unstemmed | Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification |
| title_short | Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification |
| title_sort | using visualization to evaluate the performance of algorithms for multivariate time series classification |
| topic | multivariate time series classification UEA archive time series visualization deep learning classifiers |
| url | https://www.mdpi.com/2306-5729/10/5/58 |
| work_keys_str_mv | AT edgaracuna usingvisualizationtoevaluatetheperformanceofalgorithmsformultivariatetimeseriesclassification AT roxanaaparicio usingvisualizationtoevaluatetheperformanceofalgorithmsformultivariatetimeseriesclassification |