Abstract：
:Delay embedding-a method for reconstructing dynamical systems by delay coordinates-is widely used to forecast nonlinear time series as a model-free approach. When multivariate time series are observed, several existing frameworks can be applied to yield a single forecast combining multiple forecasts derived from various embeddings. However, the performance of these frameworks is not always satisfactory because they randomly select embeddings or use brute force and do not consider the diversity of the embeddings to combine. Herein, we develop a forecasting framework that overcomes these existing problems. The framework exploits various "suboptimal embeddings" obtained by minimizing the in-sample error via combinatorial optimization. The framework achieves the best results among existing frameworks for sample toy datasets and a real-world flood dataset. We show that the framework is applicable to a wide range of data lengths and dimensions. Therefore, the framework can be applied to various fields such as neuroscience, ecology, finance, fluid dynamics, weather, and disaster prevention.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Okuno S,Aihara K,Hirata Ydoi
10.1038/s41598-019-57255-4subject
Has Abstractpub_date
2020-01-20 00:00:00pages
664issue
1issn
2045-2322pii
10.1038/s41598-019-57255-4journal_volume
10pub_type
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