期刊
SCIENTIFIC REPORTS
卷 5, 期 -, 页码 -出版社
NATURE RESEARCH
DOI: 10.1038/srep14750
关键词
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资金
- Department of Defense, Strategic Environment Research and Development Program [RC-2509]
- Lenfest Ocean Program [00028335]
- National Science Foundation [DEB-1020372]
- NSF-NOAA Comparative Analysis of Marine Ecosystem Organization (CAMEO) program [NA08OAR4320894/CAMEO]
- National Science Foundation Graduate Research Fellowships
- Environmental Protection Agency Science
- European Research Council Advanced Grant
- Sugihara Family Trust
- Deutsche Bank-Jameson Complexity Studies Fund
- McQuown Chair in Natural Science
An important problem across many scientific fields is the identification of causal effects from observational data alone. Recent methods (convergent cross mapping, CCM) have made substantial progress on this problem by applying the idea of nonlinear attractor reconstruction to time series data. Here, we expand upon the technique of CCM by explicitly considering time lags. Applying this extended method to representative examples (model simulations, a laboratory predator-prey experiment, temperature and greenhouse gas reconstructions from the Vostok ice core, and long-term ecological time series collected in the Southern California Bight), we demonstrate the ability to identify different time-delayed interactions, distinguish between synchrony induced by strong unidirectional-forcing and true bidirectional causality, and resolve transitive causal chains.
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