# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 5 Conclusions  
Precise stock forecasting can assist investors in mitigating the risks associated with their investments, of which predictive capabilities and accuracy is restricted by traditional forecasting models. To improve the existing issues, this study proposed a novel stock prediction model, defined as EL-MTSA, based on a deep-learning neural network and ensemble technology optimization. Firstly, the EL-MTSA model aggregates the prediction results of multiple sub-models (LSTM, GRU, Bi-LSTM, and CNN-GRU) and leverages diversity to reduce bias and variance. This significantly enhances the model's fitting performance and overall prediction accuracy while improving its robustness and generalization capability. Then, the novel evaluation index via market-wide sentiment analysis is proposed to enhance the forecasting performance of the stock prediction model. Those index indicators are derived from positive and negative counts as well as neutral news from media reports to capture the psychological states and behavioral patterns of market participants, which can reveal market expectations for future trends, and thereby facilitate a more comprehensive understanding of market dynamics and reduce prediction bias. To verify the effectiveness of the EL-MTSA model in practical investment scenarios, we conducted many comparative experiments on three stock indices, the CSI 300, SSE 50, and CSI A50 indices. Abundant results demonstrated that the proposed EL-MTSA model achieved a superior predictive performance, surpassing various comparison models. On the CSI 300 dataset, EL-MTSA's prediction results of three indicators, including MAE, SMAPE, RMSE, are specifically decreased by 8.85%, 10.80%, and 10.38%, respectively, as well as overperforming forecasting results on the SSE 500 and CSI A50 dataset. Additionally, we assessed the model's generalization capability across market cycles through visualization. The results indicated that the EL-MTSA model exhibits stability and reliability in capturing market dynamics. However, due to data size limitations and variant complexity, our proposed model still remains fraught with challenges rooted in autonomous data mining capabilities and the real-world unpredictability application to stock markets. Moreover, the proposed model also has insufficient performance in the aspects of multi-factor intrinsic law mining and single stock prediction. In future work, we will focus on hybrid modeling architecture, improve probabilistic frameworks, and adaptive normalization techniques to address these limitations. Furthermore, we plan to further evaluate the model's generalization capability by simulating various market conditions, including financial crises, economic recessions, and public health crises. We will utilize simulated data to test the model's performance under different market scenarios and intend to supplement the relevant analysis in the next version of our research. Conceptualization, X.J. ; methodology, J.K. and X.Z. ; software, X.Z. and X.J. ; validation, W.H. ; formal analysis, J.K. and X.Z. ; investigation and data curation, W.H. ; writing--original draft preparation, J.K. and X.Z. ; writing--review and editing, J.K. and X.Y. ; supervision, project administration, J.K. and X.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported in part by the Project of ALL China Federation of Supply and Marketing Cooperatives (No. 202407), National Natural Science Foundation of China (Nos. 62433002, 62473008, 62476014 and 62173007), Beijing Nova Program (No. 20240484710), Beijing Scholars Program (No. 099), and Beijing Municipal University Teacher Team Construction Support Plan (No. BPHR20220104), Open Projects of the Institute of Systems Science, Beijing Wuzi University (No. BWUISS07), Science, Technology, and Innovation Program of National Regional Medical Center of Taiyuan Bureau of Science and Technology (No. 202243). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.