期刊
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
卷 62, 期 -, 页码 202-212出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.cnsns.2018.02.027
关键词
Transfer entropy; Multiscale analysis; Information flow; Causality
类别
资金
- China National Science [61703029, 61671048]
- MOE (Ministry of Education in China) Project of Humanities and Social Sciences [16YJC910007]
- Beijing Outstanding Talent Project [B16H200060]
- Beijing Social Science [17YJC024]
In this paper, we propose a novel multiscale transfer entropy (MTE) approach to quantify the information transfer of time series on multiple time scales. The MTE combines the advantages of both the multiscale analysis and the transfer entropy, which is able to identify directional, dynamical and scale-dependent information flows. To minimize finite size effects and to avoid spurious detection of causality, we resort to a refined time-delayed multiscale transfer entropy (TMTE) estimator given on overlapping coarse-graining. We also suggest several extensions of the TMTE, including an effective TMTE and a net TMTE. Synthetic simulations including the linear vector autoregressive (VAR) models, the long-range correlated ARFIMA processes, and the nonlinear Rossler systems are analyzed, and an application of the TMTE to daily closing prices and trading volumes of S&P 500 is studied. (C) 2018 Elsevier B.V. All rights reserved.
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