Search
Abstract
Time series classification (TSC) is the problem of categorizing time series data by using machine learning techniques. Its applications vary from cybersecurity and health care to remote sensing and human activity recognition. In this paper, we propose a novel multi-process collaborative architecture for TSC. The propositioned method amalgamates multi-head convolutional neural networks and capsule mechanism. In addition to the discovery of the temporal relationship within time series data, our approach derives better feature extraction with different scaled capsule routings and enhances representation learning. Unlike the original CapsNet, our proposed approach does not need to reconstruct to increase the accuracy of the model. We examine our proposed method through a set of experiments running on the domain-agnostic TSC benchmark datasets from the UCR Time Series Archive. The results show that, compared to a number of recently developed and currently used algorithms, we achieve 36 best accuracies out of 128 datasets. The accuracy analysis of the proposed approach demonstrates its significance in TSC by offering very high classification confidence with the potential of making inroads into plentiful future applications.
Citations
-
2 9
CrossRef
-
0
Web of Science
-
2 9
Scopus
Authors (4)
-
Zhiwen Xiao
- Chengdu University of Information Technology, Chengdu, China
-
Xingtao Xu
- China University of Mining and Technology, Xuzhou, China
-
Haoxi Zhang
- Chengdu University of Information Technology, Chengdu, China
Cite as
Full text
- Publication version
- Accepted or Published Version
- License
-
open in new tab
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
KNOWLEDGE-BASED SYSTEMS
no. 220,
ISSN: 0950-7051 - Language:
- English
- Publication year:
- 2021
- Bibliographic description:
- Xiao Z., Xu X., Zhang H., Szczerbicki E.: A new multi-process collaborative architecture for time series classification// KNOWLEDGE-BASED SYSTEMS -Vol. 220, (2021), s.106934-
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.knosys.2021.106934
- Verified by:
- Gdańsk University of Technology
seen 147 times
Recommended for you
Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France
- N. N. Navnath,
- K. Chandrasekaran,
- A. Stateczny
- + 2 authors
Investigating Feature Spaces for Isolated Word Recognition
- G. Korvel,
- G. Tamulevicus,
- P. Treigys
- + 2 authors
Investigation of the 16-year and 18-year ZTD Time Series Derived from GPS Data Processing
- Z. Bałdysz,
- G. Nykiel,
- M. Figurski
- + 2 authors
Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
- K. Thiagarajan,
- M. Manapakkam Anandan,
- A. Stateczny
- + 2 authors