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

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 n...

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Bibliographic Details
Published in:Shanghai jiao tong da xue xue bao 2023-12, Vol.28 (6), p.809-821
Main Authors: Zhu, Changsheng, Kang, Lianghe, Feng, Wenfang
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Architecture
Computer Science
Data mining
Electrical Engineering
Engineering
Experimental methods
Fish
Iterative methods
Life Sciences
Machine learning
Materials Science
Noise prediction
Noise threshold
Predictions
Public opinion
White noise
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Summary: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.
ISSN:1007-1172
1674-8115
1995-8188
DOI:10.1007/s12204-021-2337-x