基于MPA-ELM的股票价格预测模型研究

Stock Price Forecasting Model Based on MPA-tELM

针对极限学习机(ELM)学习速率慢、易陷入局部最优且泛化能力不强等问题,提出一种基于海洋捕食者算法(MPA)和极限学习机(ELM)的组合预测模型。利用海洋捕食者算法对ELM的关键参数进行优化,降低人为因素的干扰,建立具有较高准确率的MPA-ELM股票价格预测模型。将该模型与ELM、BOA-ELM、WOA-ELM等模型的预测结果进行比较,结果证明,提出的MPA-ELM预测模型准确率更高且收敛速度更快。

For the problems of extreme learning machine(ELM),such as slow learning rate,easy to fall into local optimum and weak generalization ability,a combined forecasting model based on marine predator algorithm(MPA)and extreme learning machine(ELM)is proposed.The Marine Predator Algorithm is used to optimize the key parameters of ELM,reduce the interference of human factors,and establish the MPA-ELM stock price prediction model with high accuracy.Compared with the prediction results of ELM,BOA-ELM,WOA-ELM and other models,the experimental results show that the MPA-ELM model has higher prediction accuracy and faster convergence speed.