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+## Structured Graph Learning via Laplacian Spectral Constraints for Timeseries
+
+This repository contains the work of **Paul Martin** and **Samuel Diai** for the Project 6.8 of the Course "Time Series Learning" of Laurent Oudre.
+
+On this project, we studied a novel approach to learn a structured Graph with Laplacian constraints.
+
+Our work is directly adapted from the article **"Structured Graph Learning via Laplacian Spectral
+Constraints"** of Sandeep Kumar, Jiaxi Ying, Jose Vinicius de M. Cardoso and Daniel P. Palomar.
+
+![alt text](https://github.com/SamuelDiai/SGL/blob/master/timeseries_graph.gif "Logo Title Text 1")
+
+The repository contains : 
+* The code of the Structured Graph Learning algorithm (SGL)
+* Some tests made on very simple datasets (two moons, circles and blops) - basic_experiments.py
+* An experiment on an animal dataset - animals.py
+* An experiment on a cancer genome dataset - cancer.py
+* A notebook where we do the above experiments and plot the results.
+* A notebook (SGL_timeseries.ipynb) where we apply the SGL algorithm to timeseries data. It is directly adapted from the assignment n°6 of the course "Time Series Learning" by Charles Truong.
+* The final report of our project.
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