Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications
It addresses the problem of analyzing unlabeled time-series data for researchers and practitioners in fields such as wireless communications and IoT, but it is incremental as it is a review paper summarizing existing work.
This review synthesizes recent progress in applying autoencoders and vision transformers for unsupervised signal analysis across domains like biomedical engineering and IoT, highlighting their use in feature extraction, anomaly detection, and classification while identifying challenges in interpretability and scalability.
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.