Affiliations: [a] School of Public Finance and Taxation, Shandong University of Finance and Economics, Jinan, Shandong, China | [b] School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, Shandong, China
Correspondence:
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Corresponding author: Mei Sun, School of Public Finance and Taxation, Shandong University of Finance and Economics, Jinan, Shandong 250014, China. E-mail: [email protected].
Abstract: The accuracy of stock index prediction is of great significance to national economic development. However, because of the nonlinearity and long-term dependence of stock index data, effective prediction of future stock index price becomes a challenge. In order to solve the above problems, this paper proposes a research method of stock index time series prediction based on ensemble learning model. This method first uses an Adaboost.R2 algorithm to iteratively train multiple LSTM models and then integrates these LSTM models based on the parameters obtained by iterative training. Finally, it uses the ensemble model to predict stock index time series data. This paper uses the Shanghai Composite index, CSI 300 index and Shenzhen Composite index as experimental data sets, and uses the BP model, CNN model and LSTM model as comparative models to conduct an experimental analysis. The experimental results show that the new ensemble learning model proposed in this paper has certain advantages in the research of stock index time series prediction.
Keywords: LSTMAdaboost, R2stock index prediction, ensemble learning
