Visual Evaluation of Generative Adversarial Networks for Time Series Data

Understand

A crucial factor to trust Machine Learning (ML) algorithm decisions is a good representation of its application field by the training dataset.

• This is particularly true when parts of the training data have been artificially generated to overcome common training problems such as lack of data or imbalanced dataset.
• Over the last few years, Generative Adversarial Networks (GANs) have shown remarkable results in generating realistic data.
• However, this ML approach lacks an objective function to evaluate the quality of the generated data.