https://github.com/manitadayon/tsBNgen

https://github.com/manitadayon/tsBNgen

Last synced: 3 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/manitadayon/tsBNgen
• Owner: manitadayon
• License: mit
• Created: 2020-08-27T21:52:13.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2023-10-22T05:42:08.000Z (about 1 year ago)
• Last Synced: 2024-07-08T12:34:12.669Z (4 months ago)
• Language: Python
• Size: 173 KB
• Stars: 59
• Watchers: 2
• Forks: 10
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: License.txt

Awesome Lists containing this project

• awesome-data-synthesis - tsBNgen - - [Paper](https://arxiv.org/pdf/2009.04595.pdf) (Data-driven methods / Time Series)

README

        
![GitHub](https://img.shields.io/github/license/manitadayon/tsBNgen) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tsBNgen) ![GitHub User's stars](https://img.shields.io/github/stars/manitadayon?style=flat-square) ![GitHub forks](https://img.shields.io/github/forks/manitadayon/tsBNgen?logo=GitHub)

![PyPI](https://img.shields.io/pypi/v/tsBNgen)

## If you would like to buy me a coffee



**tsBNgen: A Python Library to Generate Time Series Data Based on an Arbitrary Bayesian Network Structure**

[Description](#Description)

[Citation](#Citaton)

[Features](#Features)

[Instruction](#Instruction)

[License](#License)

----

### **Description**

#### tsBNgen is a Python package to generate time series data based on an arbitrary Bayesian Network Structures. 

---

### **Citation**

 #### If you find this package useful or if you use it in your research or work please consider citing it as follows:

```

@article{tadayon2020tsbngen,

  title={tsBNgen: A Python Library to Generate Time Series Data from an Arbitrary Dynamic Bayesian Network Structure},

  author={Tadayon, Manie and Pottie, Greg},

  journal={arXiv preprint arXiv:2009.04595},

  year={2020}

}

```

----

### **Features**

 - It handles discrete nodes, continous nodes and hybrid (Mixture of discrete and continuous) network.

 - It uses multinomila distribution for the discrete nodes and Gaussian distribution for the continuous nodes.

 - It handles arbitrary Bayesian network structure.

 - It supports arbitrary loopback values.

 - The code can be modified easily to handle arbitrary static and temporal structures.

---

### **Instruction**

 To run this code either clone this repo or use the package distribution in PyPI using the following commands:

```python

pip install tsBNgen

```

 Then Run through the set of examples in 

 

 > **Time_Series_Generation_Examples.ipynb**

For more information on how to use the package please visit the following:

1. Watch my Youtube tutorial (I go over the package) 

 Watch the videos

3. Original paper 

4. Documentation in PDF available in this repository.

### **License**

This software is released under the MIT liecense.