基于黄金正弦算法的支持向量回归机参数优化及其在股市中的应用
Support Vector Regression Parameters Optimization using Golden Sine Algorithm and its application in stock market
摘要：
支持向量机建模是机器学习中一种新的分类方法，在小样本、高维的预测问题上表现出良好的性能。后来，它推广到支持向量回归（SVR）回归问题。实现可靠性的一大挑战是选择适当的参数。在这里，一种新的黄金正弦算法（GSA）的支持向量回归机提出了适当的选择参数。为了进行比较，在均方误差和平均绝对百分比误差的基础上，利用Yahoo财经网站上的科技公司历史股价数据，将该算法与其他11种元启发式算法进行了性能比较.结果表明，该算法是有效的调整参数，确实是竞争力的精度和计算时间。
英文摘要：
Support vector machine modeling is a new approach in machine learning for classification showing good performance on forecasting problems of small samples and high dimensions. Later, it promoted to Support Vector Regression (SVR) for regression problems. A big challenge for achieving reliable is the choice of appropriate parameters. Here, a novel Golden sine algorithm (GSA) based SVR is proposed for proper selection of the parameters. For comparison, the performance of the proposed algorithm is compared with eleven other meta-heuristic algorithms on some historical stock prices of technological companies from Yahoo Finance website based on Mean Squared Error and Mean Absolute Percent Error. The results demonstrate that the given algorithm is efficient for tuning the parameters and is indeed competitive in terms of accuracy and computing time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
