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 demonstrate that encoding time series through Visibility Graphs allows us to capture the behavior of the time series and the nonlinear interactions between observations well. The results reveal that the proposed methodology outperforms more established approaches.