TITLE:
Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications
AUTHORS:
Hossein Ahmadi, Sajjad Emdadi Mahdimahalleh, Arman Farahat, Banafsheh Saffari
KEYWORDS:
Unsupervised Learning, Autoencoders, Vision Transformers, Time-Series Analysis, Signal Processing, Representation Learning, Anomaly Detection, Wireless Signals, Biomedical Signals, Radar, IoT
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.17 No.2,
May
28,
2025
ABSTRACT: 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.