A Causality Based Feature Selection Approche for Multivariate Time Series Forecasting

Implementation of the GFSM (Granger causality Featur Selection Method):

Hmamouche, Y.; Casali, A.; Lakhal, L. Causality based feature selection approach for multivariate time series forecasting. In Proceedings of the International Conference on Advances in Databases, Knowledge, and Data Applications, Barcelona, Spain, 21–25 May 2017

Prerequisites

This implementation requires the R software and other R packages. After installing R, these packages can be installed manually from http://cran.us.r-project.org, or using the requirements.R script as follows:

Rscript requirements.R

Example

The code of the GFSM algorithm is located in src/gfsm.R. But it requires the causality matrix of the multivariate time series. Thus, another script for computing this matrix is in src/causality_graph.R. We provide an example two execute these two steps for selecting predictors for variables of stock-and-watson-2012 datasets. The example can be executed as follows:

sh main.sh

Authors

• Youssef Hmamouche