基于小波包和神经网络的股票价格预测模型

股票价格是大量因素影响的综合结果,波动规律异常复杂,即使是神经网络这样强大的非线性预测工具也不堪胜任对其的准确预测。本文利用小波包理论将价格波动序列最优地分解为一系列规律较易掌握的子波动,对原始价格波动的预测也就分成神经网络对各子波动的预测。实证研究结果表明,这种小波包和神经网络相结合的股票价格预测模型预测精度明显高于小波和神经网络相结合以及直接利用价格波动预测的同类神经网络模型。

Stock Price Forecasting Model Base don Wavelet Packet and Neural Network

Stock price is the result of effects of a large number of factors The rule of price change is too complex to learn by neural network This paper uses wavelet packet theory to decompose price series into several subseries whose rule is relatively easy to learn by NN So the task of forecasting stock price is decomposed into forecasting in the decomposed series using NN The result of case study shows that the model of wavelet packet integrated neural network is more effect than same neural network model and wavelet integrated neural network model.