Advanced Machine Learning Techniques for Pattern Recognition in Time Series (PRISMA)
The goal of PRISMA is to advance the state of the art in machine learning theory and algorithms that exploit temporal information. PRISMA aims to develop a general probabilistic framework to deal with temporal dynamics in time series, based on Bayesian graphical models for temporal pattern recognition. Furthermore, PRISMA aims to extend the state-of-the-art in kernel methods and machine learning algorithms that deal explicitly with the temporal variable in pattern recognition problems. The developed theoretical framework will be evaluated in applications from several key areas of the modern digital society.