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

arXiv: https://arxiv.org/abs/2504.16972 · HTML: https://mlsanity.com/paper/2504.16972/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 90 references that are in the map.
- First posted on arXiv 23 Apr 2025.

## Builds on (most cited first, 23 of 23)

- [Recent Advances in Autoencoder-Based Representation Learning](https://mlsanity.com/paper/1812.05069/) (2018)
- [A Survey of Visual Transformers](https://mlsanity.com/paper/2111.06091/) (2021)
- [Time Series as Images: Vision Transformer for Irregularly Sampled Time Series](https://mlsanity.com/paper/2303.12799/) (2023)
- [ViTs for SITS: Vision Transformers for Satellite Image Time Series](https://mlsanity.com/paper/2301.04944/) (2023)
- [Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing](https://mlsanity.com/paper/2310.18653/) (2023)
- [Harnessing Vision Models for Time Series Analysis: A Survey](https://mlsanity.com/paper/2502.08869/) (2025)
- [Unsupervised Learning for Human Sensing Using Radio Signals](https://mlsanity.com/paper/2207.02370/) (2022)
- [Positional Encoding in Transformer-Based Time Series Models: A Survey](https://mlsanity.com/paper/2502.12370/) (2025)
- [Unsupervised Deep Learning for IoT Time Series](https://mlsanity.com/paper/2302.03284/) (2023)
- [From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecasting](https://mlsanity.com/paper/2403.11047/) (2024)
- [Enhancing Automatic Modulation Recognition through Robust Global Feature Extraction](https://mlsanity.com/paper/2401.01056/) (2024)
- [Reconstruction-based Out-of-Distribution Detection for Short-Range FMCW Radar](https://mlsanity.com/paper/2302.14192/) (2023)
- [Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing](https://mlsanity.com/paper/2401.02764/) (2024)
- [NMformer: A Transformer for Noisy Modulation Classification in Wireless Communication](https://mlsanity.com/paper/2411.02428/) (2024)
- [Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder](https://mlsanity.com/paper/2504.14879/) (2025)
- [Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data](https://mlsanity.com/paper/2004.06806/) (2020)
- [Transformer Masked Autoencoders for Next-Generation Wireless Communications: Architecture and Opportunities](https://mlsanity.com/paper/2401.06274/) (2024)
- [DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signals](https://mlsanity.com/paper/2501.11538/) (2025)
- [Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift](https://mlsanity.com/paper/2403.04036/) (2024)
- [SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTs](https://mlsanity.com/paper/2407.00851/) (2024)
- [Comparison of Autoencoder Encodings for ECG Representation in Downstream Prediction Tasks](https://mlsanity.com/paper/2410.02937/) (2024)
- [Few-Shot Radar Signal Recognition through Self-Supervised Learning and Radio Frequency Domain Adaptation](https://mlsanity.com/paper/2501.03461/) (2025)
- [TSViT: A Time Series Vision Transformer for Fault Diagnosis](https://mlsanity.com/paper/2311.06916/) (2023)

## Built on by (most cited first, 3 of 3)

- [Sanitizing Manufacturing Dataset Labels Using Vision-Language Models](https://mlsanity.com/paper/2506.23465/) (2025)
- [Advanced DOA Regulation with a Whale-Optimized Fractional Order Fuzzy PID Framework](https://mlsanity.com/paper/2508.12487/) (2025)
- [Vision-Language Models for Infrared Industrial Sensing in Additive Manufacturing Scene Description](https://mlsanity.com/paper/2512.11098/) (2025)

## Abstract (arXiv)

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.