Intelligent analysis of economic parameters of European countries

Engineers today are often bound to work with complex data, with valuable knowledge to be extracted and models to be built for predicting future behavior. With a given set of economic parameters for the European countries, the goal of this paper is to analyze if they can form meaningful community structure. To this end, we propose a network science approach to economic time series analysis. The first step is the creation of corresponding graphs as the most adequate data representation that can capture multiple relations and dependencies among entities of interest. The preprocessing steps for creating a graph include calculating correlation coefficients for the parameters, which are used for weighing the graph. The weighted graphs are then analyzed for their underlying structure using clustering algorithms which produce various communities depending on the settings employed. Experiments are performed using different correlation calculations for graph weighing and different settings for cluster extraction and the produced communities are analyzed in terms of their quality, both in view of modularity score and economic meaning. Results show that the proposed approach successfully captures meaningful economic relationships between European countries and can be a solid base on which future, more complex analysis can be build.