1 research outputs found
tsBNgen: A Python Library to Generate Time Series Data from an Arbitrary Dynamic Bayesian Network Structure
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