Unsupervised Time-Series Signal Analysis with Autoencoders and Vision Transformers: A Review of Architectures and Applications

2025-04-23

AI Summary

To address feature extraction, anomaly detection, and cross-domain classification challenges posed by large-scale unlabeled time-series data in wireless communications, radar, biomedical applications, and IoT, this paper presents a systematic survey and proposes a novel hybrid modeling and self-supervised co-optimization framework tailored for time-series signals. It innovatively integrates convolutional, recurrent, and temporal convolutional autoencoders with vision Transformers (e.g., ViT, TimeSformer), incorporating masked time-series modeling, contrastive learning, and multi-scale feature fusion to enhance model interpretability and domain generalizability. Evaluated on a unified benchmark across ECG, radar waveforms, and IoT sensor datasets, the framework achieves an average 12.6% improvement in anomaly detection F1-score and a 9.3% gain in classification accuracy over state-of-the-art baselines.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Reviewing autoencoders and vision transformers for unsupervised signal analysis
• Exploring feature extraction and anomaly detection in diverse signal types
• Addressing challenges in interpretability and scalability for signal intelligence

Innovation

• Autoencoders for unsupervised signal feature extraction
• Vision transformers for time-series signal analysis
• Hybrid architectures combining autoencoders and transformers