基于极限学习机的股票价格预测

Stock Price Forecasting Based on Extreme Learning Machine

极限学习机(Extreme Learning Machine,ELM)是一种新型的单馈层神经网络算法,克服了传统的误差反向传播方法需要多次迭代,算法的计算量和搜索空间大的缺点,只需要设置合适的隐含层节点个数,为输入权和隐含层偏差进行随机赋值,一次完成无需迭代。研究表明股票市场是一个非常复杂的非线性系统,需要用到人工智能理论、统计学理论和经济学理论。本文将极限学习机方法引入股票价格预测中,通过对比支持向量机(Support Vector Machine,SVM)和误差反传神经网络(Back Propagation Neural Network,BP神经网络),分析极限学习机在股票价格预测中的可行性和优势。结果表明极限学习机预测精度高,并且在参数选择及训练速度上具有较明显的优势。

Extreme learning machine （ ELM ） is a new learning algorithm of single-hidden layer feed-forward neural network （ SLFNs） , and overcomes the disadvantages of the classical learning algorithm in neural network method ’ s multiple iterations , huge search space and a large number of calculations , only needs to set the appropriate numbers of hidden layer nodes , assigns the weight of input and deviation of hidden layers without iteration .Research shows that the stock market is a very complex non-linear system , we need to use artificial intelligence theory , statistics theory and economic theory to study the stock price forecast . In this paper , ELM is introduced in predicting the stock price , and by comparing with SVM and BP , we analyze its feasibility and advantage in stock price prediction .The experiment results show that ELM is of high accuracy of prediction and obvious advanta -ges in parameter selection and learning speed .