论文标题
TSBNGEN：一个Python库，用于从任意动态贝叶斯网络结构生成时间序列数据
tsBNgen: A Python Library to Generate Time Series Data from an Arbitrary Dynamic Bayesian Network Structure
论文作者
论文摘要
合成数据广泛用于各个域。这是因为许多现代算法需要大量的数据进行有效的培训，而数据收集和标签通常是一个耗时的过程，并且容易出错。此外，由于其性质，一些现实世界的数据是机密的，无法共享。贝叶斯网络是一种广泛用于对现实过程中不确定性建模的概率图形模型。动态贝叶斯网络是一个特殊的贝叶斯网络类，它们对时间和时间序列数据进行建模。在本文中，我们介绍了TSBNGEN，这是一个Python库，该库可生成基于任意动态贝叶斯网络的时间序列和顺序数据。可以从https://github.com/manitadayon/tsbngen下载软件包，文档和示例。
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.
