tsBNgen swMATH ID: 35076 Software Authors: Manie Tadayon, Greg Pottie Description: 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 Homepage: https://arxiv.org/abs/2009.04595 Source Code: https://github.com/manitadayon/tsBNgen Dependencies: Python Keywords: Machine Learning; arXiv_cs.LG; Signal Processing; arXiv_eess.SP; Python; Generate; Time Series Data; Dynamic Bayesian networks; Synthetic Data Related Software: Python Cited in: 0 Publications Standard Articles 1 Publication describing the Software Year tsBNgen: A Python Library to Generate Time Series Data from an Arbitrary Dynamic Bayesian Network Structure Manie Tadayon, Greg Pottie 2020