改进支持向量机的股票价格预测研究

Study on Stock Price Forecasting Based on Improved Support Vector Machine

为了提高股票价格预测精度,提出一种改进支持向量机的股票价格预测模型。该模型利用粒子群算法的全局寻优能力对支持向量机参数进行优化,以提高股票价格的预测精度,采用具体股票价格数据对模型性能进行测试。结果表明,改进支持向量机能够对股票价变化趋势进行预测,是一种有效、高精度的股票价格预测模型。

In order to improve the precision of stock price forecasting, in this paper, a stock price forecasting model based on improved support vector machine was proposed. Particle swarm optimization algorithm which has global optimization ability was used to optimize the parameters of support vector machine to improve the precision of prediction of stock price. The performance of the model was tested by stock price data, and the result showed that the improved support vector machine could describe the stock price change trend, and It was an effective, high precision stock price forecasting model.