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中文摘要：
针对股票价格构成的时间序列具有随机性与偶然性,传统的单一模型很难满足建模要求的问题,提出一种基于小波和神经网络相结合的股票预测模型。将股票价格进行小波分解成尺度不同的分层数据,分别利用Elman神经网络预测各层数据,将各层的预测结果使用BP神经网络合成最终预测结果。通过实际的股票价格对该模型进行验证,结果表明,该组合模型具有较高的预测效果,可以提高股票价格预测的准确率。
英文摘要：
Considering that time series of stock price is randomness and contingency and traditional method in the stock price prediction using single model couldn＇t satisfied with practical demand.A new stock price prediction model is proposed, which combines the wavelet transform and artificial neural network.Firstly, the stock price data are decomposed to different scale data using discrete wavelet transform.Then, Elman neural network are used respectively to predict each layer series.Finally, each layer prediction results are combined into the final result through BP neural network.The result of the prediction is verified with the practical stock price and show the new model has better predictive precision.
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