# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 4 Experimental Results and Discussion
### Experimental Results and Analyses
#### 4.4.6 Cross-Market-Cycle Generalization Capability Assessment  
To ensure the robustness of the model and the reliability of its predictions, rolling window cross-validation was employed for data validation. In this process, strict adherence to the temporal nature of the time-series data was maintained, ensuring that the chronological order of the data was preserved. The training and testing sets were allocated in an 8:2 ratio, allowing the model to leverage sufficient training data while maintaining strict isolation of the test data. Special attention was given to distinguishing between the training and testing sets in both temporal dimensions and operational steps, including independent normalization procedures, to prevent any form of data leakage or temporal inconsistency. This design ensured that the test data remained entirely unseen during the training process. The test set spanned 340 trading days and comprehensively covered diverse market conditions, including economic recession, growth, and stability phases not present in the training data. This section provides a detailed evaluation of the model's cross-market-cycle generalization capability, with a focus on its performance across economic recession, growth, and stability phases. The methodology for rolling window cross-validation is also elaborated herein.  
Rolling window cross-validation is a validation method specifically designed for time-series data. Its core idea is to dynamically adjust the window position to sequentially evaluate the model's predictive capability across different time periods. First, the data is divided into a training set and a test set, and then the sliding window operation is carried out separately for each set. For a time series of length \(T\), starting at time point \(t=1\), the training data is \([1,w]\) and the testing data is \([w+1,w+h]\). The window is then  
Figure 9: Comparative analysis of simulated investments.  
shifted forward by \(s\) steps, resulting in a new training set \([1+s,w+s]\) and a new testing set \([w+s+1,w+s+h]\). This process is repeated until the end position of the testing set exceeds the time series length T, until \(w+s\cdot k+h\geq T\), where \(k\) is the number of steps. As shown in Figure 10.  
To evaluate the generalization capability of the EL-MTSA model across market cycles (recessionary, growth, and stable phases), we plotted the model's predicted values against actual market values from 9 August 2019 to 31 December 2020. The chart highlights recessionary phases (green shaded regions), growth phases (red shaded regions), and stable phases (gray shaded regions), as shown in Figure 11.  
Figure 11: Line Chart of Predicted vs. Actual Market Values.  
Figure 10: Schematic diagram of rolling window cross-validation.  
The period under analysis encompasses significant market declines and rebounds, particularly during 17 January 2020 to 5 February 2020, and 6 March 2020 to 24 March 2020, driven by COVID-19 pandemic-induced panic selling, which created pronounced bearish market conditions. During this turmoil, certain sectors, such as pharmaceuticals, remote work, and online education, exhibited resilience or growth due to heightened demand. This led to capital reallocation from pandemic-impacted sectors to those benefiting from the crisis, driving sector-specific growth. As market sentiment stabilized, strategic buying at market lows contributed to subsequent rallies. Additionally, following effective domestic pandemic control measures and gradual economic reopening, heightened expectations of economic recovery prompted investor reallocation into equities, as evidenced by the upward market trajectory from 15 June 2020 to 19 August 2020.  
Our analysis demonstrates that the EL-MTSA model exhibits robustness and effectiveness in capturing market dynamics across all phases--recessionary, growth, and stable--underscoring its reliability, even during periods of heightened market volatility.