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.

Some methods, such as generative adversarial network¹, are proposed to generate time series data. However, GAN is hard to train and might not be stable; besides, it requires a large volume of data for efficient training.

This article will introduce the tsBNgen, a python library, to generate synthetic time series data based on an arbitrary dynamic Bayesian network structure.

Following is a list topics discussed in this article.

• Introduction
• Features
• Instruction
• Example
• Conclusion

#python #synthetic-data #probabilistic-models #bayesian-statistics #machine-learning