Optimization of Mixture Models on time series networks encoded by Visibility Graphs: An analysis of the US electricity market
by Carlo Mari, Cristiano Baldassari.
This repository contains the code to reproduce all the results reported in the paper Optimization of Mixture Models on time series networks encoded by Visibility Graphs: An analysis of the US electricity market.
We propose a fully unsupervised network-based methodology for estimating Gaussian Mixture Models on financial time series by maximum likelihood using the Expectation-Maximization algorithm. Visibility Graph-structured information of observed data is used to initialize the algorithm. The proposed methodology is applied to the US wholesale electricity market. We will show that encoding time series through Visibility Graphs allows us to capture well the behavior of the time series and the nonlinear interactions between observations. The results reveal that the proposed methodology outperforms more established approaches.
The provided Python notebook contains the code that implements the method of initialization we propose in the paper and covers all the paper's workflow.
You can download a copy of all the files in this repository by cloning the git repository:
git clone https://github.com/cbaldassari/time_series_network