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Generating Synthetic Time Series Data for Cyber-Physical Systems

arXiv 2404.08601 ↗First posted 12 Apr 2024cs.LGcited by 0

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Abstract (arXiv)

Data augmentation is an important facilitator of deep learning applications in the time series domain. A gap is identified in the literature, demonstrating sparse exploration of the transformer, the dominant sequence model, for data augmentation in time series. A architecture hybridizing several successful priors is put forth and tested using a powerful time domain similarity metric. Results suggest the challenge of this domain, and several valuable directions for future work.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 12 Apr 2024.

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22 of 37 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
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  10. 2020On Layer Normalization in the Transformer Architecturecited by 906
  11. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  12. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 329
10 more
  1. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 221
  2. 2021TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Upcited by 162
  3. 2018Synthesizing Tabular Data using Generative Adversarial Networkscited by 92
  4. 2022Transformers in Time-series Analysis: A Tutorialcited by 45
  5. 2022TTS-GAN: A Transformer-based Time-Series Generative Adversarial Networkcited by 41
  6. 2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
  7. 2024Time Weaver: A Conditional Time Series Generation Modelcited by 35
  8. 2023TSGBench: Time Series Generation Benchmarkcited by 24
  9. 2022TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentationcited by 20
  10. 2019The Wasserstein-Fourier Distance for Stationary Time Seriescited by 18

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