Learning Iterated Function Systems from Time Series of Partial Observations
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866918127239954432 |
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| author | Gibson, Emilia Lamb, Jeroen S. W. |
| author_facet | Gibson, Emilia Lamb, Jeroen S. W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13794 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Iterated Function Systems from Time Series of Partial Observations Gibson, Emilia Lamb, Jeroen S. W. Dynamical Systems 37M10, 37H05, 37H99, 37B10, 37B55, 65P9910 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. |
| title | Learning Iterated Function Systems from Time Series of Partial Observations |
| topic | Dynamical Systems 37M10, 37H05, 37H99, 37B10, 37B55, 65P9910 |
| url | https://arxiv.org/abs/2508.13794 |