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基于小波包去噪的股价组合预测模型

发表时间:2014-07-15  浏览量:1602  下载量:673
全部作者: 池贝,牛明飞
作者单位: 兰州大学数学与统计学院
摘 要: 检验基于小波包去噪和反向传播(back propagation,BP)模型、自回归移动平均(auto regressive and moving average,ARMA)模型、指数平滑 (exponential smoothing,ES)模型的组合模型对股价预测的有效性。选取的数据为中国建设银行2010年至2012年的日收盘价。首先,对建设银行的原始数据建立3个单个模型,分别为BP模型、ARMA模型、ES模型,再利用粒子群算法优化组合模型的权重,发现组合模型的预测效果优于单个模型的预测效果。然后,对经过小波包去噪的建设银行数据再建立以上3个模型及组合模型,结果显示:基于小波包去噪的组合模型的预测效果优于未去噪的组合模型,从而说明了基于小波包去噪的组合预测模型在股价预测方面的有效性。
关 键 词: 应用数学;股价预测;小波包去噪;反向传播模型;自回归移动平均模型;指数平滑模型
Title: A hybrid model based on wavelet packet denoising in stock price forecasting
Author: CHI Bei, NIU Mingfei
Organization: School of Mathematics and Statistics, Lanzhou University
Abstract: The purpose of this article is to use the hybrid model based on the wavelet packet denoising, back propagation (BP) model, auto regressive and moving average (ARMA) model and exponential smoothing (ES) model to examaine the effectiveness of forecasting stock price. The selection data is daily closing price of China Construction Bank during 2010-2012. Firstly, with the original data of China Construction Bank, three single models, i.e. BP model, ARMA model, ES model are set up respectively, then uses particle swarm optimization algorithm to combine the weight of models. It is found that the effectiveness in predicting the stock price of the hybrid model is better than any other single model. Then with the data after wavelet packet denoising of China Construction Bank, the same hybrid model is set up. The results show that, the model’s prediction effectiveness based on the wavelet packet denoising is more better than the unwavelet packet denoising model. Therefore, the hybrid model based on wavelet packet denoising is effective for stock price forecasting.
Key words: applied mathematics; stock price forecasting; wavelet packet denoising; back propagation model; auto regressive and moving average model; exponential smoothing model
发表期数: 2014年7月第13期
引用格式: 池贝,牛明飞. 基于小波包去噪的股价组合预测模型[J]. 中国科技论文在线精品论文,2014,7(13):1307-1316.
 
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