Time-series Generative Adversarial Networks
Govind Sharma (20817244)
A time-series model should not only be good at learning the overall distribution of temporal features within different time points, but it should also be good at capturing the dynamic relationship between the temporal variables across time. The popular autoregressive approach in time-series or sequence analysis is generally focused on minimizing the error involved in multi-step sampling improving the temporal dynamics of data. In this approach, the distribution of sequences is broken down into a product of conditional probabilities. The deterministic nature of this approach works well for forecasting but it is not very promising in a generative setup. The GAN approach when applied on time-series directly simply tries to learn p(X|t) using generator and discriminator setup but this fails to leverage the prior probabilities like in the case of the autoregressive case. This paper proposes a novel GAN architecture that combines the two approaches (unsupervised GANs and supervised autoregressive) that allow a generative model to have the ability to preserve temporal dynamics along with learning the overall distribution. This mechanism has been termed as Time-series Generative Adversarial Network or TimeGAN. To incorporate supervised learning of data into the GAN architecture, this approach makes use of an embedding network that provides a reversible mapping between the temporal features and their latent representations. The key insight of this paper is that the embedding network is trained in parallel with the generator/discriminator network. This approach leverages the flexibility of GANs together with the control of the autoregressive model resulting in significant improvements in the generation of realistic time-series.