Computational social science is more than just large repositories of digital data and the
computational methods needed to construct and analyse them. It also represents a …

Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …

The M4 Competition follows on from the three previous M competitions, the purpose of which
was to learn from empirical evidence both how to improve the forecasting accuracy and how …

Time series forecasting is an extensively studied subject in statistics, economics, and
computer science. Exploration of the correlation and causation among the variables in a …

Machine Learning (ML) methods have been proposed in the academic literature as
alternatives to statistical ones for time series forecasting. Yet, scant evidence is available …

Gamification is increasingly employed in learning environments as a way to increase
student motivation and consequent learning outcomes. However, while the research on the …

Due to the significantly complex and nonlinear behavior of li-ion batteries, forecasting the
state of charge (SOC) of the batteries is still a great challenge. Therefore, accurate SOC …

In a capitalist system, consumers, investors, and corporations orient their activities toward a
future that contains opportunities and risks. How actors assess uncertainty is a problem that …

The mean absolute percentage error (MAPE) is one of the most widely used measures of
forecast accuracy, due to its advantages of scale-independency and interpretability …

The field of energy forecasting has attracted many researchers from different fields (eg,
meteorology, data sciences, mechanical or electrical engineering) over the last decade …