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Timeseries, in particular signal timeseries, are distinct in many respects - so GANs working on images may not work for timeseries. Since other questions asking on data augmentation, GANs have progressed, for example:

All above have a theme in common: images. -- This said: can GANs be used for timeseries data augmentation in 2019? If so, any examples of implementations that would work well?

Additional info:

I'm training a CNN-RNN EEG seizure (binary) classifier. Data specs:

  • 10 minute segments sampled at 400-Hz --> 240000 timesteps per sample
  • 4520 samples: 660 positive (seizure), 3860 negative (non-seizure)
  • 16 channels in each sample; samples stem from 3 patients
  • ~1.74E10 datapoints in total
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