汇率预测问题的集成多目标粒子群优化方法

T. Dinh, V. Vu, L. Bui
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引用次数: 0

摘要

本文提出了一种用于货币汇率链预测的集成多目标粒子群优化方法(称为EMPSO)。该算法主要分为两个阶段。第一阶段使用多目标粒子群优化算法寻找一组最优粒子(称为leader)。第二阶段使用软投票集合法,使用这些领导者共同计算最终结果。这里使用的两个目标函数是预测误差和粒子多样性。本研究使用的实证数据是六套不同的货币汇率。通过与其他进化算法和其他多目标粒子群算法的比较结果表明,该算法在实验数据集上可以获得更好的稳定性结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An ensemble multi-objective particle swarm optimization approach for exchange rates forecasting problem
In this paper, the authors propose an ensemble multi-objective particle swarm optimisation approach (named EMPSO) for forecasting the currency exchange rate chain. The proposed algorithm consists of two main phases. The first phase uses a multi-objective particle swarm optimisation algorithm to find a set of the best optimal particles (named leaders). The second phase then uses these leaders to jointly calculate the final results by using the soft voting ensemble method. The two objective functions used here are predictive error and particle diversity. The empirical data used in this study are six different sets of currency exchange rates. Through comparison results with other evolutionary algorithms and other multi-objective PSO algorithms, the proposed algorithm shows that it can achieve better as well as more stability results on experimental data sets.
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