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 .%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。
展开▼
