tsBNgen, a Python Library to Generate Synthetic Data From an Arbitrary Bayesian Network.

When we think of machine learning, the first step is to acquire and train a large dataset. This problem is faced by hundreds of developers, especially for projects which have no previous developments. Certain GAN (Generative Adversarial Network) models, specifically Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN), have been introduced to produce realistic real-valued multi-dimensional time-series data. This paper brings the solution to this problem via the introduction of tsBNgen, a Python library to generate time series and sequential data based on an arbitrary dynamic Bayesian network. This package lets the developers and researchers generate time series data according to the random model they want.