Bayesian and graph theory approaches to develop strategic early warning systems for the milk market

Abstract

This paper presents frameworks for developing a Strategic EarlyWarning System allowing the estimatation of the future state of the milk market. Thus, this research is in line with the recent call from the EU commission for tools which help to better address such a highly volatile market. We applied different multivariate time series regression and Bayesian networks on a pre-determined map of relations between macro economic indicators. The evaluation of our findings with root mean square error (RMSE) performance score enhances the robustness of the prediction model constructed. Finally, we construct a graph to represent the major factors that effect the milk industry and their relationships. We use graph theoretical analysis to give several network measures for this social network; such as centrality and density.