Researchers From Imperial College London introduce TsT-GAN: A | Data Scientology

Researchers From Imperial College London introduce TsT-GAN: A Novel Framework For Training Time-Series Generative Models

Nowadays, data is considered a fuel in the data analytics field. The real-time applications require time series data for analysis and future prediction. But all these applications usually lack the necessary, sufficient data for analysis. Hence, various data augmentation techniques need to be adopted. Researchers from Imperial College London introduce a framework called TsT-GAN, based on generative adversarial networks (GAN), that are utilized to augment the time-series data. It aims to fulfill the various objectives like capturing the steps of the conditional distribution of real-time sequences and creating a model that joins the distribution of all the real-time sequences.

The paper’s significant contribution is to develop the model consisting of a generator that can produce entire joint distributions considering the distribution conditions. The training framework can be applied to any time series dataset that quantitively results in a standard method that can be trained on the synthetic test on a realistic approach while qualitatively using t-SNE. 

Continue Reading | Check out the *paper* and *related codes*.

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