Graph Learning: Causal Graph Process (CGP) & Sparse Vector Autoregressive model (SVAR)

Table of Contents

• Graph Learning: Causal Graph Process (CGP) & Sparse Vector Autoregressive model (SVAR)
  • Table of Contents
  • Intorduction
  • Directory Tree
  • Requirements
  • Description
  • Reference
  • Author

Intorduction

This work contains the impelmentation and comparison of two graph learning algorithms, Causal Graph Process (CGP) [1] and Sparse Vector Autoregressive model (SVAR) [2]. These two graph learning methods can be used to derive the graph representation among a large number of unstructured time series data, and then make predictions on the future data.

Directory Tree

CGP_and_SVAR/
├─ CGP.m ............... Main function for CGP graph learning
├─ CGP_plotgraph.m ..... Plot CGP graph learning result
├─ CGP_prediction.m .... Predict future data using CGP
├─ CGP_MSE_compare.m ... Plot MSE Comparison for different orders of CGP
├─ SVAR.m .............. Main function for SVAR graph learning
├─ SVAR_plotgraph.m .... Plot SVAR graph learning result
├─ SVAR_prediction.m ... Predict future data using SVAR
├─ SVAR_MSE_compare.m .. Plot MSE Comparison for different orders of SVAR
├─ CGP/ ................ Directory for storing CGP graph learning data
├─ SVAR/ ............... Directory for storing SVAR graph learning data
└─ Slides/ ............. 

Requirements

• MATLAB: 2019a or later
• CVX: Version 2.2 or later

Description

For more details, please refer to this slides.

Reference

[1] J. Mei and J.M.F. Moura, “Signal processing on graphs: Causal modeling of unstructured data” IEEE Trans. on Signal Processing, vol. 65(8), pp. 2077−2092, 2017.

[2] A. Davis, Richard & Zang, Pengfei & Zheng, Tian. (2012). “Sparse Vector Autoregressive Modeling.” Journal of ComputaIonal and Graphical StaIsIcs.

Author

Name : Hong-Ming Chiu

Email : hmchiu2 [at] illinois.edu

Website : https://hong-ming.github.io