Estimating structure of multivariate systems with genetic algorithms for nonlinear prediction
Estimating structure of multivariate systems with genetic algorithms for nonlinear prediction
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DOI：
10.1103/physreve.80.066208
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发表时间：
2009-12-01
影响因子：
2.4
通讯作者：
Sato, Haruki
中科院分区：
文献类型：
--
作者：
Suzuki, Tomoya;Ueoka, Yuta;Sato, Haruki
虽然我们经常可以观察到许多元素的时间序列数据，但这些元素并不总是相互作用的。本文提出了一种仅用时间序列数据估计观测要素之间相互依赖关系的方案，该方案有助于选择必要要素来优化多变量预测模型。由于这种估计是一种组合优化问题，我们采用遗传算法来缓和这一问题。通过仿真验证了该方法的有效性，该方法能够识别多变量系统的交互作用，提高了系统的预测精度。特别地，我们的方法可以应用于具有非平稳性质和结构动态变化的实际外汇市场的预测。
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.