Predicting Stock Closing Price with Stock Network Public Opinion Based on AdaBoost-AAFSA-Elman Model and CEEMDAN Algorithm  

作　　者：朱昶胜 康亮河 冯文芳 ZHU Changsheng;KANG Lianghe;FENG Wenfang(School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China;School of Economics and Management,Lanzhou University of Technology,Lanzhou 730050,China;College of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,China)

机构地区：[1]School of Computer and Communication,Lanzhou University of Technology,Lanzhou 730050,China [2]School of Economics and Management,Lanzhou University of Technology,Lanzhou 730050,China [3]College of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2023年第6期809-821,共13页上海交通大学学报（英文版）

基　　金：the Development Fund Project of Information Science and Technology College of Gansu Agricultural University of China(No.GAU-XKFZJJ-2020-02)。

摘　　要：To solve low prediction accuracy of Elman in predicting stock closing price,the model of adaptive boosting(AdaBoost)-improved artificial fish swarm algorithm(AAFSA)-Elman based on complete ensemble em-pirical mode decomposition with adaptive noise(CEEMDAN)is proposed.By adding different white noise to the original data,CEEMDAN algorithm is used to decompose attributes serial selected by Boruta algorithm and text mining.To optimize the weight and threshold of Elman,self-adaption step length and view scope are used to improve artificial fish swarm algorithm(AFSA).AdaBoost algorithm is used to compose 5 weak AAFSA-Elman predictors into a strong predictor by continuous iteration.Experiments show that the mean absolute percentage error(MAPE)of AdaBoost-AAFSA-Elman model reduces from 4.9423%to 1.2338%.This study provides an experimental method for the prediction of stock closing price based on network public opinio.

关 键 词：network public opinion CEEMDAN ADABOOST AFSA ELMAN 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]