mlsanityData to 14 Oct 2026

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

arXiv 2504.16972 ↗First posted 23 Apr 2025cs.LG cs.AI cs.CVcited by 3

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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.

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

23 of 90 references.

References · 23

  1. 2018Recent Advances in Autoencoder-Based Representation Learningcited by 133
  2. 2021A Survey of Visual Transformerscited by 117
  3. 2023Time Series as Images: Vision Transformer for Irregularly Sampled Time Seriescited by 79
  4. 2023ViTs for SITS: Vision Transformers for Satellite Image Time Seriescited by 56
  5. 2023Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensingcited by 48
  6. 2025Harnessing Vision Models for Time Series Analysis: A Surveycited by 26
  7. 2022Unsupervised Learning for Human Sensing Using Radio Signalscited by 14
  8. 2025Positional Encoding in Transformer-Based Time Series Models: A Surveycited by 14
  9. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  10. 2024From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecastingcited by 9
  11. 2024Enhancing Automatic Modulation Recognition through Robust Global Feature Extractioncited by 7
  12. 2023Reconstruction-based Out-of-Distribution Detection for Short-Range FMCW Radarcited by 6
11 more
  1. 2024Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensingcited by 6
  2. 2024NMformer: A Transformer for Noisy Modulation Classification in Wireless Communicationcited by 6
  3. 2025Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encodercited by 6
  4. 2020Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Datacited by 5
  5. 2024Transformer Masked Autoencoders for Next-Generation Wireless Communications: Architecture and Opportunitiescited by 4
  6. 2025DenoMAE: A Multimodal Autoencoder for Denoising Modulation Signalscited by 4
  7. 2024Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shiftcited by 3
  8. 2024SAFE: a SAR Feature Extractor based on self-supervised learning and masked Siamese ViTscited by 3
  9. 2024Comparison of Autoencoder Encodings for ECG Representation in Downstream Prediction Taskscited by 2
  10. 2025Few-Shot Radar Signal Recognition through Self-Supervised Learning and Radio Frequency Domain Adaptationcited by 2
  11. 2023TSViT: A Time Series Vision Transformer for Fault Diagnosiscited by 1

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

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  3. 2025Vision-Language Models for Infrared Industrial Sensing in Additive Manufacturing Scene Descriptioncited by 0