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Search Results (3)

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Keywords = UEA archive

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25 pages, 14600 KiB  
Article
Using Visualization to Evaluate the Performance of Algorithms for Multivariate Time Series Classification
by Edgar Acuña and Roxana Aparicio
Data 2025, 10(5), 58; https://doi.org/10.3390/data10050058 - 24 Apr 2025
Viewed by 347
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 [...] Read more.
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. Full article
(This article belongs to the Topic Future Trends and Challenges in Data Mining Technology)
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14 pages, 931 KiB  
Article
Time Series Classification with InceptionFCN
by Saidrasul Usmankhujaev, Bunyodbek Ibrokhimov, Shokhrukh Baydadaev and Jangwoo Kwon
Sensors 2022, 22(1), 157; https://doi.org/10.3390/s22010157 - 27 Dec 2021
Cited by 12 | Viewed by 5784
Abstract
Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research [...] Read more.
Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm-based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well-known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state-of-the-art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time-series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive. Full article
(This article belongs to the Section Internet of Things)
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26 pages, 874 KiB  
Article
F4: An All-Purpose Tool for Multivariate Time Series Classification
by Ángel López-Oriona and José A. Vilar
Mathematics 2021, 9(23), 3051; https://doi.org/10.3390/math9233051 - 27 Nov 2021
Cited by 5 | Viewed by 2814
Abstract
We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying generating processes. This goal has barely been addressed in the literature. F4 consists of two steps. First, a set of [...] Read more.
We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying generating processes. This goal has barely been addressed in the literature. F4 consists of two steps. First, a set of features based on the quantile cross-spectral density and the maximum overlap discrete wavelet transform are extracted from each series. Second, a random forest is fed with the extracted features. An extensive simulation study shows that F4 outperforms some powerful classifiers in a wide variety of situations, including stationary and nonstationary series. The proposed method is also capable of successfully discriminating between electrocardiogram (ECG) signals of healthy subjects and those with myocardial infarction condition. Additionally, despite lacking shape-based information, F4 attains state-of-the-art results in some datasets of the University of East Anglia (UEA) multivariate time series classification archive. Full article
(This article belongs to the Special Issue Data Mining for Temporal Data Analysis)
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