# Research on Ensemble Learning-Based Feature Selection Method for Time-Series Prediction
## 5 Conclusions  
This paper proposes a feature selection method based on ensemble learning. Firstly, we clearly defined the application of ensemble learning to feature selection and selected five feature selection methods based on the \"good but different\" principle for integration. When dealing with specific datasets, we determined the weights of each feature selection method through K-fold cross-validation. Ultimately, we adopted a feature score weighting approach, synthesizing the advantages of each method to select the final feature subset. Subsequently, a series of experiments were conducted to validate the effectiveness of this method. We utilized stock data from ICBC, GD Power, and China Unicom to construct a dataset, employing ensemble learning to filter out the top five features with the most significant impact on prediction results from the dataset's twenty features. Following this, we employed LSTM to build a time-series prediction model, comparing the predictions of the five features selected by each method with those selected by the ensemble learning method. The experimental results demonstrate the superior performance of the ensemble learning method in terms of the MAE and MSE metrics, effectively showcasing the method's efficacy and robustness. In summary, this study not only offers profound insights into the theoretical application of ensemble learning in feature selection but also validates its effectiveness in practical applications across various domains. It provides valuable insights for research and practice in related fields. Future research directions could include further optimizing the determination methods for ensemble learning weights and extending the application of this method to other time-series data domains, such as electricity and weather. These efforts will contribute to deeper insights and beneficial revelations for both the research and practical applications in the field of feature selection. Conceptualization, D.H. and Z.L. ; methodology, D.H.; software, Z.L. ; validation, D.H., Z.L. and D.W.; formal analysis, D.W.; investigation, Z.L. ; resources, D.H.; data curation, D.H.; writing--original draft preparation, Z.L. ; writing--review and editing, D.H.; visualization, D.W.; supervision, D.W.; project administration, D.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Selection Methods** & **Dataset** & **MAE** & **MSE** \\ \hline \multirow{3}{*}{GBDT} & ICBC & 0.2177 & 0.0618 \\  & GD Power & **0.2548** & **0.0667** \\  & China Unicom & 0.1469 & 0.0318 \\ \hline \multirow{3}{*}{RFE} & ICBC & **0.192** & **0.049** \\  & GD Power & **0.2548** & **0.0667** \\  & China Unicom & 0.1469 & 0.0318 \\ \hline \multirow{3}{*}{XGBoost} & ICBC & 0.2462 & 0.0789 \\  & GD Power & 0.2635 & 0.0713 \\  & China Unicom & 0.1469 & 0.0318 \\ \hline \multirow{3}{*}{Pearson} & ICBC & 0.235 & 0.0716 \\  & GD Power & 0.2723 & 0.0761 \\  & China Unicom & 0.1438 & 0.0305 \\ \hline \multirow{3}{*}{RF} & ICBC & 0.2177 & 0.0618 \\  & GD Power & **0.2548** & **0.0667** \\  & China Unicom & **0.1386** & **0.0284** \\ \hline \multirow{3}{*}{Ensemble Learning} & ICBC & **0.192** & **0.049** \\  & GD Power & **0.2548** & **0.0667** \\ \cline{1-1}  & China Unicom & **0.1386** & **0.0284** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Predicted prices for typical stocks under different feature selection methods. The best values are shown in bold. **Institutional Review Board Statement:** The research was approved by the Research Ethics Committee of National University of Defense Technology. Approval code: LWBMSP202307112728. Approval date: 11 July 2023. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** Data for this study are available from the corresponding author on request. The data are not publicly available due to privacy restrictions.