How can you apply GANs for time-series data generation

Question

Can you tell me, with the help of code snippets, how you can apply GANs to time-series data generation?

Answer 1

To apply GANs for time-series data generation, you can use a sequence-to-sequence architecture with recurrent neural networks (RNNs) or 1D convolutional layers. 

Here is the code example you can refer to:

In the code, we are using the following approaches:

• Generator: Maps random latent vectors to synthetic time-series data.
• Discriminator: Classifies sequences as real or fake.
• Training Loop: Alternates between training the generator and discriminator with adversarial loss.

Hence, by referring to the above, you can apply GANs for time-series data generation.