Estimating structure of multivariate systems with genetic algorithms for nonlinear prediction

Tomoya Suzuki, Yuta Ueoka, and Haruki Sato
Phys. Rev. E 80, 066208 – Published 17 December 2009

Abstract

Although we can often observe time-series data of many elements, these elements do not always interact with each other. This paper proposes a scheme to estimate the interdependency among observed elements only by time-series data, which is useful for selecting essential elements to optimize multivariate prediction model. Because this estimation is a sort of combinatorial optimization problems, we applied the genetic algorithm as a method to moderate this problem. Through some simulations, we confirmed performance of our method, which can identify interaction of multivariate system and can improve its prediction accuracy. Especially, our method can be applied to predict real foreign-exchange markets even if system has nonstational property and its structure changes dynamically.

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  • Received 4 June 2009

DOI:https://doi.org/10.1103/PhysRevE.80.066208

©2009 American Physical Society

Authors & Affiliations

Tomoya Suzuki1, Yuta Ueoka2, and Haruki Sato3

  • 1Department of Intelligent Systems Engineering, College of Engineering, Ibaraki University, 4-12-1 Nakanarisawa-cho, Hitachi, Ibaraki 316-8511, Japan
  • 2Graduate School of Engineering, Doshisha University, 1-3 Tatara-Miyakodani, Kyotanabe-shi, Kyoto 610-0321, Japan
  • 3Department of Information System Design, Faculty of Science and Engineering, Doshisha University, 1-3 Tatara-Miyakodani, Kyotanabe-shi, Kyoto 610-0321, Japan

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Issue

Vol. 80, Iss. 6 — December 2009

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January 14, 2016

The editors of Physical Review E are pleased to announce that the journal’s stated scope has been expanded to explicitly include the term “Biological Physics.”

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