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- W1566435334 abstract "Recently, a rigorous yet concise formula was derived to evaluate information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate and a noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient. It is verified with autoregressive models and applied to a real financial analysis problem. An unusually strong one-way causality is identified from IBM (International Business Machines Corporation) to GE (General Electric Company) in their early era, revealing to us an old story, which has almost faded into oblivion, about Seven Dwarfs competing with a giant for the mainframe computer market." @default.
- W1566435334 created "2016-06-24" @default.
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- W1566435334 date "2015-08-17" @default.
- W1566435334 modified "2023-10-18" @default.
- W1566435334 title "Normalizing the causality between time series" @default.
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- W1566435334 doi "https://doi.org/10.1103/physreve.92.022126" @default.
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