摘要
针对Elman神经网络在基于股市网络舆情的收盘价预测中存在的收敛速度慢且预测精度低的问题,提出了结合基于自适应噪声的完全集合经验模态分解(CEEMDAN)的改进鲸鱼优化算法(IWOA)结合Elman神经网络预测模型。首先,通过文本挖掘技术对上海证券交易所股票价格综合指数(SSE)180股的网络舆情进行挖掘和量化,并利用Boruta算法筛选重要属性以降低属性集的复杂度;然后,通过CEEMDAN算法在属性集中添加一定数量特定方差的白噪声,实现属性序列的分解与降噪;同时,利用自适应权重改进鲸鱼优化算法(WOA)以增强其全局搜索及局部开采能力;最后,利用WOA在迭代过程中不断优化Elman神经网络的初始权重和阈值。结果表明:比起单独使用Elman神经网络,所提模型的平均绝对误差(MAE)从358.8120降低至113.0553;与未采用CEEMDAN算法的原始数据集相比,该模型的平均绝对百分比误差(MAPE)从4.9423%降低到1.44531%,说明所提模型有效提高了预测精度,为股市网络舆情的预测提供了一种有效的实验方法。
Focused on the issue that Elman neural network has slow convergence speed and low prediction accuracy in the closing price prediction based on the network public opinion of the stock market,a prediction model combining Improved Whale Optimization Algorithm(IWOA)and Elman neural network was proposed,which is based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)algorithm.Firstly,text mining technology was used to mine and quantify the network public opinions of Shanghai Stock Exchange(SSE)180 shares,and in order to reduce the complexity of attribute set,Boruta algorithm was used to select the important attributes.Then,CEEMDAN algorithm was used to add a certain number of white noises with specific variances in order to realize the decomposition and noise reduction of the attribute sequence.At the same time,in order to enhance the global search and local mining capabilities,adaptive weight was used to improve Whale Optimization Algorithm(WOA).Finally,the initial weights and thresholds of Elman neural network were optimized by WOA in the iterative process.The results show that,compared to Elman neural network,the proposed model has the Mean Absolute Error(MAE)reduced from 358.8120 to 113.0553;compared to the original dataset without CEEMDAN algorithm,the proposed model has the Mean Absolute Percentage Error(MAPE)reduced from 4.9423%to 1.44531%,demonstrating that the model effectively improves the prediction accuracy and provides an effective experimental method for predicting the network public opinion of stock market.
作者
朱昶胜
康亮河
冯文芳
ZHU Changsheng;KANG Lianghe;FENG Wenfang(College of Computer and Communications,Lanzhou University of Technology,Lanzhou Gansu 730050,China;School of Economics and Management,Lanzhou University of Technology,Lanzhou Gansu 730050,China)
出处
《计算机应用》
CSCD
北大核心
2020年第5期1501-1509,共9页
journal of Computer Applications
关键词
网络舆情
文本挖掘
鲸鱼优化算法
ELMAN神经网络
network public opinion
text mining
Whale Optimization Algorithm(WOA)
Elman neural network
