Politecnico di Torino (logo)

Predictive relevance in dynamical systems

Davide Straziota

Predictive relevance in dynamical systems.

Rel. Luca Dall'Asta, Matteo Marsili, Iacopo Mastromatteo, Michael Benzaquen. Politecnico di Torino, Corso di laurea magistrale in Physics Of Complex Systems (Fisica Dei Sistemi Complessi), 2021

PDF (Tesi_di_laurea) - Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives.

Download (3MB) | Preview

Complex systems have been the object of studies across diverse fields, comprising both hard and soft sciences. The data revolution and the enormous amount of data available in recent years allowed for their quantitative analysis. According to the problem under investigation, one can focus on their stationary properties, thus adopting a static description, or analyze their dynamics and their out-of-equilibrium properties. The goal of this thesis is to investigate the dynamical behavior of a complex system using dimensional reduction, a technique aiming to reduce the number of degrees of freedom of the system by constructing a synthetic, more effective representation. Here we focus on a fully unsupervised approach to dimensional reduction by coupling clustering techniques with recent ideas of maximally informative representations. The performance of prediction algorithms built around these ideas has been tested. The relation between prediction power and information content, of the clustering label set, has been examined by considering two numerical experiments. The first experiment is designed for finding the best agglomerative methods' inter-cluster linkage, while the second experiment for investigating whether, starting from a system state representation, it has been possible to detect time series underlying generative model properties. The experimental results have been used for drawing general conclusions about this dimensional reduction technique.

Relators: Luca Dall'Asta, Matteo Marsili, Iacopo Mastromatteo, Michael Benzaquen
Academic year: 2020/21
Publication type: Electronic
Number of Pages: 67
Corso di laurea: Corso di laurea magistrale in Physics Of Complex Systems (Fisica Dei Sistemi Complessi)
Classe di laurea: New organization > Master science > LM-44 - MATHEMATICAL MODELLING FOR ENGINEERING
Aziende collaboratrici: Econophysics & Complex Systems: Capital Fund Management Research Chair at Ecole polytechnique
URI: http://webthesis.biblio.polito.it/id/eprint/19145
Modify record (reserved for operators) Modify record (reserved for operators)