Competence Centre on Composite Indicators and Scoreboards

The Composite Indicators & Scoreboards Explorer is a European Commission’s interactive tool to explore and visualise data from over 150 indices, scoreboards and dashboards that have been devised...

In this manuscript, we propose a Machine Learning approach to predict the magnitude of future stock price variations for individual companies of the S&P 500 index. Sets of lexicons are...

In recent years, machine learning algorithms have been successfully employed to leverage the potential of identifying hidden patterns of financial markets behavior, and, consequently, have...

Press releases represent a valuable resource for financial trading and have long been exploited by researchers for the development of automatic stock price predictors. We hereby propose...

A number of artificial intelligence and machine learning problems need to be formulated within a directional space, where classical Euclidean geometry does not apply or needs...

Global sensitivity analysis is primarily used to investigate the effects of uncertainties in the input variables of physical models on the model output. This work investigates the use of global sensitivity analysis...

Comparison studies of global sensitivity analysis (GSA) approaches are limited in that they are performed on a single model or a small set of test functions, with a limited set...

One of the most powerful and versatile system identification frameworks of the last three decades is the NARMAX/NARX approach, which is based on a nonlinear discrete-time representation. Recent advances in machine...

One of the most versatile and powerful algorithms for the identification of nonlinear dynamical systems is the NARMAX (Nonlinear Auto-regressive Moving Average with eXogenous inputs) approach. The model represents the current...

We compare the convergence properties of two different quasi-random sampling designs – Sobol’s quasi-Monte Carlo, and Latin supercube sampling in variance-based global sensitivity analysis...