兰州大学机构库 >数学与统计学院
基于小波包去噪的股价组合预测模型
Alternative TitleA combined stock price forecasting model based on the wavelet packet denoising
池贝
Thesis Advisor牛明飞
2014-06-01
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name硕士
Keyword股价预测 小波包去噪 BP神经网络模型 ARMA模型 指数平滑模型 粒子群算法 组合模型
Abstract本文的目的是检验基于小波包去噪的结合BP 神经网络模型、ARMA 模型和指数平滑模型的组合模型对股价预测的有效性. 选取的数据是中国建设银行2011 年至2013 年三年的股票的日收盘价. 首先对建设银行的原始数据建立了三个单个的模型, 分别为BP 神经网络模型、ARMA 模型和指数平滑模型, 再利用粒子群算法优化组合模型的权重, 从而建立组合模型, 并用三个单个模型和组合模型对数据进行预测, 发现组合模型的预测效果优于单个模型的预测效果. 然后对原始数据进行小波包去噪,对去噪后的数据分别建立以上三个单个模型和组合模型,并进行预测,结果显示,基于小波包去噪的组合模型的预测效果更加优于未去噪的组合模型,从而说明了本文建立的基于小波包去噪的组合模型在股价预测方面的有效性.
Other AbstractThe purpose of this article is to use the combined model which based on the wavelet packet denoising combining with BP neural network model, ARMA model and exponential smoothing (ES) model to examine the effectiveness of forecasting stock price. The selection datas are the three years’ daily closing price of the China construction bank’s stock price in 2011-2013. First, with the original data of construction bank, three single models, BP model, ARMA model and ES model are set up respectively. Then particle swarm optimization (PSO) algorithm is used to optimize the weights of the combined model. And we construct the combined model and predict for datas by making use of the three single models and the combined model. It finds that the effectiveness in predicting the stock price of the combined model is better than any other single model. Then with the datas after wavelet packet denoising of China construction bank, the same three single models and the combined model are set up, and predict for the obtained datas. According to the results, based on the wavelet packet denoising model’s prediction effectiveness is more better than the unwavelet packet denoising model. Thereby the combined model based on wavelet packet denoising is effective for stock price forecasting.
URL查看原文
Language中文
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/225095
Collection数学与统计学院
Recommended Citation
GB/T 7714
池贝. 基于小波包去噪的股价组合预测模型[D]. 兰州. 兰州大学,2014.
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