# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 2 Related Work  
### Traditional Stock Prediction Models  
Traditional financial time series forecasting methods primarily include technical analysis and econometric models. Technical analysis [15] relies on technical indicators or their combinations to predict future stock trends. Econometric models, such as the autoregressive integrated moving average (ARIMA) [16] and the generalized autoregressive conditional heteroskedasticity (GARCH) [17], are grounded in time series analysis and statistical principles. However, these methods often fail to account for nonlinear relationships and complex structures within the data, limiting their ability to fully capture the dynamic characteristics of the stock market.  
Compared with traditional models, machine learning methods have demonstrated stronger predictive capabilities in financial time series forecasting. These methods leverage computational algorithms and extensive historical data to identify patterns and trends, enabling more accurate predictions. Common machine learning techniques include linear regression [18], support vector machines (SVM) [19], decision trees [20], and random forests [21]. While machine learning has achieved notable success in stock prediction, the inherent noise and uncertainty in stock market data--driven by market sentiment fluctuations and policy changes--pose significant challenges. These factors can interfere with model training and prediction, ultimately reducing accuracy.  
With the rapid advancement of the Internet and the widespread adoption of smart devices, deep learning models have emerged as powerful tools for financial analysis. Deep learning offers advantages such as automated feature extraction, effective handling of complex nonlinear relationships, and strong generalization capabilities. For financial data, deep learning methods, including long short-term memory (LSTM) [22], gated recurrent units (GRUs) [23], and convolutional neural networks (CNNs) [24], have shown significant promise. These approaches have achieved breakthroughs in stock prediction, particularly in addressing the limitations of traditional methods.  
Multivariate time series forecasting, especially in stock prediction, remains one of the most challenging tasks in data analysis. Deep learning has demonstrated encouraging results in overcoming these challenges, offering improved accuracy and robustness.  
In recent years, hybrid algorithms that integrate multiple machine learning models have become a focal point in financial stock forecasting research. Several combined networks have been successfully applied to financial time series prediction. For example, [PERSON] et al. [25] proposed a novel GA-LSTM-CNN architecture to predict daily stock prices. [PERSON] et al. [26] introduced a deep neural network algorithm combining CNNs, LSTM, and GRUs for stock market prediction. Similarly, [PERSON] et al. [27] developed an ARIMA-LightGBM hybrid model to forecast the stock trends of Greek Electric Appliances over a six-month period. These hybrid approaches highlight the potential of combining diverse methodologies to enhance predictive performance.