A multi-objective ensemble learning approach based on the non-dominated sorting differential evolution for forecasting currency exchange rates

T. Dinh, V. Vu, L. Bui
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引用次数: 6

Abstract

Currency exchange rates forecasting is paid a considerable attention of the researchers in the field of forecasting. The neural network is a well-known tool in machine learning. However, two issues are always interested by the scientists: getting toward to global convergence of extreme solutions and determining the optimal weight of the network. This paper proposes the multi-objective method of ensemble learning techniques based on the non-dominated sorting differential evolution (NSDE, a kind of direction-based methods) for training neural networks and application in Foreign Exchange forecasting problems. Two objectives of the selected model are defined based on the Mean Squared Errors and Diversity respectively, in which we used the concept of fitness-sharing based diversity. We experimented the model on four data sets of currency and compared with some of the others that the research community has announced. Through the performance forecasting indicators to show that our new method gives outstanding forecasting results.
基于非支配排序差分进化的多目标集成学习方法用于货币汇率预测
货币汇率预测一直是预测领域研究人员十分关注的问题。神经网络是机器学习中一个众所周知的工具。然而,有两个问题一直是科学家们感兴趣的:极值解的全局收敛和网络最优权值的确定。本文提出了一种基于非支配排序差分进化(non- dominant sorting differential evolution, NSDE,一种基于方向的方法)的多目标集成学习方法,用于神经网络的训练并应用于外汇预测问题。所选模型的两个目标分别基于均方误差和多样性定义,其中我们使用了基于健身共享的多样性概念。我们在四个货币数据集上实验了这个模型,并与研究界已经公布的其他一些数据集进行了比较。通过业绩预测指标表明,新方法具有较好的预测效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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