Stock index time series prediction based on ensemble learning model

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.