In this study, we discuss the creation of high-quality synthetic time-series datasets for one of the largest financial institutions in the world, and the methods we designed to assess the accuracy and privacy of our models and data. The temporal, ordered nature of time series data can help track and forecast future trends, which unsurprisingly, has enormous utility for business planning and investing. However, due to regulations and the inherent security risks that come with sharing data between individuals and organizations, much of the value that could be gleaned from it remains inaccessible. Here, Gretel’s work demonstrates that synthetic data can help close this gap while preserving privacy. By generating synthetic time-series data that are generalizable and shareable amongst diverse teams, we can give financial institutions a competitive edge and the power to explore a whole new world of opportunities.
Developers can test our methods by opening up our example Colab Notebook, clicking “Run All”, and entering your API key to run the entire experiment, or by following along with the 3-step process outlined below!