2012/12/21 by Shohei Hidaka, Hidaka, Shohei
Computer Science · Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Complex Systems and Time Series Analysis #Dynamical Systems (math.DS) #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Methodology (stat.ME) #Neural Networks and Applications #Statistical Mechanics and Entropy #cs.IT #math.DS #math.IT #stat.ME
paper · pdf · doi:10.48550/arxiv.1212.5449
This manuscript is submitted to Proceedings of the National Academy of Sciences of the United States of America
arxiv created 2012/12/21 · openalex publication_date 2012/12/21 · arxiv updated 2012/12/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
One of the crucial steps in scientific studies is to specify dependent relationships among factors in a system of interest. Given little knowledge of a system, can we characterize the underlying dependent relationships through observation of its temporal behaviors? In multivariate systems, there are potentially many possible dependent structures confusable with each other, and it may cause false detection of illusory dependency between unrelated factors. The present study proposes a new information-theoretic measure with consideration to such potential multivariate relationships. The proposed measure, called multivariate transfer entropy, is an extension of transfer entropy, a measure of temporal predictability. In the simulations and empirical studies, we demonstrated that the proposed measure characterized the latent dependent relationships in unknown dynamical systems more accurately than its alternative measure.