Code Repository for: Symbolic regression for empirically realistic population dynamic time series
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| Format: | Recurso digital |
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2026
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| _version_ | 1866901748131561472 |
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| author | Anonymous |
| author_facet | Anonymous |
| contents | <p>In this study, we used simulated data sets that were sampled at different sampling densities, have different levels of process noise, were pre-processed using a discrete vs. continuous-time approach, and explores two cycle types (symmetric and asymmetric) to examine how symbolic regression's success is impacted by these various factors.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19339107 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Code Repository for: Symbolic regression for empirically realistic population dynamic time series Anonymous <p>In this study, we used simulated data sets that were sampled at different sampling densities, have different levels of process noise, were pre-processed using a discrete vs. continuous-time approach, and explores two cycle types (symmetric and asymmetric) to examine how symbolic regression's success is impacted by these various factors.</p> |
| title | Code Repository for: Symbolic regression for empirically realistic population dynamic time series |
| url | https://doi.org/10.5281/zenodo.19339107 |