Dia: Dimecres, 16 d'octubre de 2019
Lloc: Aula S01, Facultat de Matemàtiques i Estadística, UPC.
A càrrec de: Pablo Roldán, Yeshiva University
Títol: Topological Data Analysis of Financial Time Series
Resum: We introduce a methodology that combines topological data analysis with a machine learning technique k-means clustering in order to characterize the emerging chaotic regime in a complex system approaching a critical transition. We first test our methodology on the complex system dynamics of a Lorenz-type attractor. Then we apply it to the four major cryptocurrencies. We find early warning signals for critical transitions in the cryptocurrency markets.
Given the audience of this seminar, I will try to emphasize the connections of our methodology to dynamical systems, in particular to bifurcation theory.
This is joint work with M. Gidea (Yeshiva University) and Yuri Katz (Standard and Poors).
Reference: M. Gidea, D. Goldsmith, Y. Katz, P. Roldan, Y. Shmalo: Topological Recognition of Critical Transitions in Time Series of Cryptocurrencies, 2019, Physica A (accepted).
Last updated: Thu Oct 10 14:34:25 2019