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- W3147250550 abstract "Causality inference for time series systems has been subject to intensive researchacross many generations of physicist and, in light of the boom of computationalresources, has been increasingly applied to a wider range of areas such as biologyor finance. In this thesis we structurally compare three inference methods, GrangerCausality, Transfer Entropy, and Convergent Cross Mapping, by applying them tosynthetic nonlinear systems. While we verify that Granger Causality only detectslinear causal relations, our analysis with Fourier Transform surrogates shows thata significant amount of causality, measured by Transfer Entropy and ConvergentCross Mapping, is driven by nonlinear properties. Our study of the Lorenz attractorfurther suggests different structures for different timeframe lengths. Upon introducingmeasures for the system causality, we observe that the long-term causality of thesystem remains approximately constant with a major nonlinear component. Ona short-term scale, the causality resolution changes, which we can map to certainlocations within the attractor. We find these properties to apply to several othersynthetic nonlinear systems. The resulting framework is designed to be applicable toreal-world time series systems in order to detect unknown causality structures anddrivers in other research areas." @default.
- W3147250550 created "2021-04-13" @default.
- W3147250550 creator A5025069255 @default.
- W3147250550 date "2020-01-01" @default.
- W3147250550 modified "2023-09-26" @default.
- W3147250550 title "Causality in Time Series Systems" @default.
- W3147250550 hasPublicationYear "2020" @default.
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