Measuring dependencies between variables of a dynamical system using fuzzy affiliations
| Autor: | Wulkow, Niklas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
barycentric coordinates
000 Informatik Informationswissenschaft allgemeine Werke::000 Informatik Wissen Systeme::000 Informatik Informationswissenschaft allgemeine Werke dependency measures 500 Naturwissenschaften und Mathematik::510 Mathematik::510 Mathematik influence detection data-driven modelling Scalable Probabilistic Approximation basketball analytics Dynamical Systems (math.DS) Numerical Analysis (math.NA) 37M10 62B10 65C20 FOS: Mathematics Mathematics - Numerical Analysis Mathematics - Dynamical Systems |
| Zdroj: | AppliedMath; Volume 2; Issue 2; Pages: 284-311 |
| Popis: | A statistical, data-driven method is presented that quantifies influences between variables of a dynamical system. The method is based on finding a suitable representation of points by fuzzy affiliations with respect to landmark points using the Scalable Probabilistic Approximation algorithm. This is followed by the construction of a linear mapping between these affiliations for different variables and forward in time. This linear mapping, or matrix, can be directly interpreted in light of unidirectional dependencies, and relevant properties of it are quantified. These quantifications, given by the sum of singular values and the average row variance of the matrix, then serve as measures for the influences between variables of the dynamics. The validity of the method is demonstrated with theoretical results and on several numerical examples, covering deterministic, stochastic, and delayed types of dynamics. Moreover, the method is applied to a non-classical example given by real-world basketball player movement, which exhibits highly random movement and comes without a physical intuition, contrary to many examples from, e.g., life sciences. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|