Learning Iterated Function Systems from Time Series of Partial Observations
Dynamical Systems
2025-08-20 v1
Abstract
We develop a methodology to learn finitely generated random iterated function systems from time-series of partial observations using delay embeddings. We obtain a minimal model representation for the observed dynamics, using a hidden variable representation, that is diffeomorphic to the original system.
Cite
@article{arxiv.2508.13794,
title = {Learning Iterated Function Systems from Time Series of Partial Observations},
author = {Emilia Gibson and Jeroen S. W. Lamb},
journal= {arXiv preprint arXiv:2508.13794},
year = {2025}
}