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WNN Prediction Model of Stock Price with Input Dimensions Reduced by Rough Set

机译：WNN预测模型的股价与粗糙集的输入尺寸减少

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摘要

To improve the prediction ability of stock price, an integration prediction method based on Rough Set (RS) and Wavelet Neural Network (WNN) is proposed. First RS is used to reduce the dimensions of feature of stock price, then the WNN prediction model is established for stock price movement on the basis of feature dimension reduction; finally, the built model is applied to predict the stock price movement. The simulations on daily closing price index of SSE Composite Index indicate that, the proposed method has advantages of simple structure, strong implementation and good prediction accuracy with average correct rate 64%, and gets better stock price prediction in contrast with single neural network, genetic neural network and WNN.
机译：为了提高股价预测能力，提出了一种基于粗糙集（RS）和小波神经网络（WNN）的集成预测方法。第一个RS用于减少股票价格的特征的尺寸，然后在特征尺寸减少的基础上建立了WNN预测模型的股票价格;最后，建造的模型应用于预测股票价格运动。 SSE综合指数日收盘价指数的模拟表明，结构简单，实施强度强，预测精度平均速度为64％，与单一神经网络相比，实现了良好的预测精度，遗传神经网络和Wnn。

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