基于AdaBoost-AAFSA-Elman模型及CEEMDAN算法的股市网络舆情收盘价预测

doi:10.1007/s12204-021-2337-x

摘要/Abstract

摘要： 针对Elman神经网络算法在股市收盘价格预测中预测精度低的问题, 基于自适应噪声的完全集合经验模态分解（CEEMDAN），提出了自适应boosting（AdaBoost）算法与人工鱼群优化改进算法（AAFSA）以及 Elman神经网络的组合预测模型。CEEMDAN算法通过对Boruta算法和文本挖掘算法获得的属性集添加白噪声，实现属性序列的分解与降噪；同时利用自适应步长和视角范围对AFSA算法进行了改进，并利用改进后的AAFSA算法优化Elman算法的权值和阈值；最后利用AdaBoos算法在连续迭代的过程中将5个AAFSA-Elman弱预测器组成一个强预测器，从而提高了预测的精度。实验表明：相比 Elman神经网络，AdaBoost-AAFSA-Elman模型的平均绝对百分比误差（MAPE）从4.9423%降低到1.2338%。本研究提出的模型为基于网络舆论股票收盘价格预测提供了一种实验方法。

关键词: 股市网络舆情，CEEMDAN算法，AdaBoost算法，AFSA算法，Elman神经网络

Abstract: 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 empirical 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.

Key words: network public opinion, CEEMDAN, AdaBoost, AFSA, Elman

中图分类号:

TP391

朱昶胜1，康亮河1,3，冯文芳2. 基于AdaBoost-AAFSA-Elman模型及CEEMDAN算法的股市网络舆情收盘价预测[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 809-821.

ZHU Changsheng1 (朱昶胜)，KANG Lianghe1.3* (康亮河)，FENG Wenfang2 (冯文芳). Predicting Stock Closing Price with Stock Network Public Opinion Based on AdaBoost-AAFSA-Elman Model and CEEMDAN Algorithm[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 809-821.

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