Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910981893914624 |
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| author | Ahmadi, Hossein Mahdimahalleh, Sajjad Emdadi Farahat, Arman Saffari, Banafsheh |
| author_facet | Ahmadi, Hossein Mahdimahalleh, Sajjad Emdadi Farahat, Arman Saffari, Banafsheh |
| contents | The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised learning. This review synthesizes recent progress in applying autoencoders and vision transformers for unsupervised signal analysis, focusing on their architectures, applications, and emerging trends. We explore how these models enable feature extraction, anomaly detection, and classification across diverse signal types, including electrocardiograms, radar waveforms, and IoT sensor data. The review highlights the strengths of hybrid architectures and self-supervised learning, while identifying challenges in interpretability, scalability, and domain generalization. By bridging methodological innovations and practical applications, this work offers a roadmap for developing robust, adaptive models for signal intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16972 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications Ahmadi, Hossein Mahdimahalleh, Sajjad Emdadi Farahat, Arman Saffari, Banafsheh Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Signal Processing I.5.4; I.2.6; H.2.8 The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised learning. This review synthesizes recent progress in applying autoencoders and vision transformers for unsupervised signal analysis, focusing on their architectures, applications, and emerging trends. We explore how these models enable feature extraction, anomaly detection, and classification across diverse signal types, including electrocardiograms, radar waveforms, and IoT sensor data. The review highlights the strengths of hybrid architectures and self-supervised learning, while identifying challenges in interpretability, scalability, and domain generalization. By bridging methodological innovations and practical applications, this work offers a roadmap for developing robust, adaptive models for signal intelligence. |
| title | Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Signal Processing I.5.4; I.2.6; H.2.8 |
| url | https://arxiv.org/abs/2504.16972 |