Cargando…
Detecting Causality from Nonlinear Dynamics with Short-term Time Series
Quantifying causality between variables from observed time series data is of great importance in various disciplines but also a challenging task, especially when the observed data are short. Unlike the conventional methods, we find it possible to detect causality only with very short time series dat...
Autores principales: | Ma, Huanfei, Aihara, Kazuyuki, Chen, Luonan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5376982/ https://www.ncbi.nlm.nih.gov/pubmed/25501646 http://dx.doi.org/10.1038/srep07464 |
Ejemplares similares
-
Predicting future dynamics from short-term time series using an Anticipated Learning Machine
por: Chen, Chuan, et al.
Publicado: (2020) -
Randomly distributed embedding making short-term high-dimensional data predictable
por: Ma, Huanfei, et al.
Publicado: (2018) -
Partial cross mapping eliminates indirect causal influences
por: Leng, Siyang, et al.
Publicado: (2020) -
Detecting causality from short time-series data based on prediction of topologically equivalent attractors
por: Zhang, Ben-gong, et al.
Publicado: (2017) -
Detecting and quantifying causal associations in large nonlinear time series datasets
por: Runge, Jakob, et al.
Publicado: (2019)