Fred Hasselman - Recurrence networks approach to phase transition detection and to analysis of data from human experience
Thursday 12/01/2023, 3:30 pm (GMT+1/Warsaw time)
Hybrid meeting (via Zoom): click here to join the meeting
If you want to join the meeting in person, please contact the organizers.
Our guest will be Prof. Fred Hasselman from Radboud University .
Title: Recurrence networks approach to phase transition detection and to analysis of data from human experience
Abstract
I will discuss how to use multiplex recurrence networks to describe the dynamics of N=1 multivariate time series consisting of many different variables, often self-reports of human experience. The aim of these descriptive analyses is that they should, in principle, be usable in a clinical setting in which process monitoring is used and data accumulates on a daily basis. In such settings decisions on potential intervention activities are based on an analysis of the dynamics of the recent past. I will discuss the promises and challenges of the recurrence network approach compared to statistical models such as symptom networks based on Vector Autoregression (VAR).
Before the meeting, please read the following papers:
- Hasselman, F.W. (2022). Early Warning Signals in Phase Space: Geometric Resilience Loss Indicators From Multiplex Cumulative Recurrence Networks. [Frontiers in Physiology].
- Hasselman, F.W. & Bosman, A.M.T. (2020). Studying Complex Adaptive Systems With Internal States: A Recurrence Network Approach to the Analysis of Multivariate Time-Series Data Representing Self-Reports of Human Experience. [Front. Appl. Math. Stat.]
This talk is a part of the Traincrease Lecture Series (D4.2).
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952324.
