Model-free prognostication of non-linear time series
The study of non-linear time series with available techniques for observed complex data can extract characteristics that enable short-range forecasting without the need for model-building. Time-lagged analysis provides one suitable foundation. Among various approaches, machine learning achieved the best prognosticative results.
Authors
- Georg F. Weber (ORCID: https://orcid.org/0000-0003-3996-677X)
- Shesh Nath Rai (ORCID: https://orcid.org/0000-0002-8377-353X)
- Xiaoyong Wu
Institutions
- University of Cincinnati Medical Center (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2026-02-02
- DOI
- https://doi.org/10.1371/journal.pone.0341777
- Primary Topic
- COVID-19 epidemiological studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00