mlsanityData to 14 Oct 2026

A Spectral Enabled GAN for Time Series Data Generation

arXiv 2103.01904 ↗First posted 2 Mar 2021cs.LG stat.MLcited by 6

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Abstract (arXiv)

Time dependent data is a main source of information in today's data driven world. Generating this type of data though has shown its challenges and made it an interesting research area in the field of generative machine learning. One such approach was that by Smith et al. who developed Time Series Generative Adversarial Network (TSGAN) which showed promising performance in generating time dependent data and the ability of few shot generation though being flawed in certain aspects of training and learning. This paper looks to improve on the results from TSGAN and address those flaws by unifying the training of the independent networks in TSGAN and creating a dependency both in training and learning. This improvement, called unified TSGAN (uTSGAN) was tested and comapred both quantitatively and qualitatively to its predecessor on 70 benchmark time series data sets used in the community. uTSGAN showed to outperform TSGAN in 80% of the data sets by the same number of training epochs and 60% of the data sets in 3/4th the amount of training time or less while maintaining the few shot generation ability with better FID scores across those data sets.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 28 of its 59 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

28 of 59 references.

References · 28

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  3. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,988
  4. 2016Improved Techniques for Training GANscited by 2,970
  5. 2017Improved Training of Wasserstein GANscited by 2,235
  6. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  7. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  8. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
  9. 2017Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictionscited by 804
  10. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  11. 2017Wasserstein GANcited by 639
  12. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 565
16 more
  1. 2016Time Series Classification from Scratch with Deep Neural Networks: A Strong Baselinecited by 360
  2. 2019MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesiscited by 354
  3. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 333
  4. 2018WaveGlow: A Flow-based Generative Network for Speech Synthesiscited by 309
  5. 2018Adversarial Audio Synthesiscited by 225
  6. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
  7. 2017Model-Free Renewable Scenario Generation Using Generative Adversarial Networkscited by 51
  8. 2018EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalscited by 48
  9. 2018T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Samplingcited by 29
  10. 2017Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveformscited by 27
  11. 2019Multi-objective training of Generative Adversarial Networks with multiple discriminatorscited by 24
  12. 2017SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generationcited by 20
  13. 2017Stabilizing GAN Training with Multiple Random Projectionscited by 15
  14. 2016Gradient-Based Multiobjective Optimization with Uncertaintiescited by 11
  15. 2019Quick and Easy Time Series Generation with Established Image-based GANscited by 9
  16. 2020Controlled time series generation for automotive software-in-the-loop testing using GANscited by 4

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2.

6 papers.

  1. 2023NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imagingcited by 19
  2. 2023Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Surveycited by 9
  3. 2024TimEHR: Image-based Time Series Generation for Electronic Health Recordscited by 8
  4. 2021Leveraging Image-based Generative Adversarial Networks for Time Series Generationcited by 4
  5. 2022Leveraging Statistical Shape Priors in GAN-based ECG Synthesiscited by 4
  6. 2024Synthesis of pulses from particle detectors with a Generative Adversarial Network (GAN)cited by 0