  Synthetic data is widely used in various domains. This is because many modern
algorithms require lots of data for efficient training, and data collection and
labeling usually are a time-consuming process and are prone to errors.
Furthermore, some real-world data, due to its nature, is confidential and
cannot be shared. Bayesian networks are a type of probabilistic graphical model
widely used to model the uncertainties in real-world processes. Dynamic
Bayesian networks are a special class of Bayesian networks that model temporal
and time series data. In this paper, we introduce the tsBNgen, a Python library
to generate time series and sequential data based on an arbitrary dynamic
Bayesian network. The package, documentation, and examples can be downloaded
from https://github.com/manitadayon/tsBNgen.
