Forex Prediction Based on SVR Optimized by Artificial Fish Swarm Algorithm

Ma Li, Fan Suohai
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引用次数: 12

Abstract

Taking the radial basis function as a kernel function, a prediction model is developed based on the support vector regression machine (SVR). The optimization of the model parameters, including penalty factor and kernel function variance, is realized by the artificial fish swarm algorithm. The model is used to predict nine foreign exchange rate data with updating and rolling. At the same time, simulating by the cross validation, genetic algorithm, particle swarm optimization algorithm and then evaluating the results from the total error (TE), relative error (RE), absolute root mean square error (ARMSE) and correct trend rate (CTR) comprehensively, the comparison shows that the errors of the model based on SVR optimized by artificial fish swarm algorithm are all minimum and CTR are maximum. In the end, in order to improve the convergence speed and precision further, the self-adaption artificial fish swarm algorithm is presented which is joined the attenuation factor and based on the average distance visual. The result is ideal. Therefore, SVR optimized by the improved artificial fish swarm algorithm can be effectively used in forex prediction.
基于人工鱼群算法优化的SVR外汇预测
以径向基函数为核函数,建立基于支持向量回归机(SVR)的预测模型。采用人工鱼群算法对惩罚因子和核函数方差等模型参数进行优化。利用该模型对9个具有更新和滚动的外汇汇率数据进行了预测。同时,通过交叉验证、遗传算法、粒子群优化算法进行仿真,然后从总误差(TE)、相对误差(RE)、绝对均方根误差(ARMSE)和正确趋势率(CTR)四个方面对结果进行综合评价,结果表明,人工鱼群算法优化的基于SVR的模型误差均最小,CTR最大。最后,为了进一步提高收敛速度和精度,提出了一种加入衰减因子和基于平均距离视觉的自适应人工鱼群算法。结果是理想的。因此,改进的人工鱼群算法优化后的支持向量回归算法可以有效地用于外汇预测。
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