# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 1 Introduction  
The application of machine learning technology to predict stock trends is one of the most significant areas of research in the financial domain, attracting extensive attention from researchers [1]. Accurate stock prediction can provide substantial benefits to investors. However, unlike time series with clear regularity and periodicity, such as temperature, transportation, or electricity data, stock price fluctuations are influenced by numerous factors [2], making stock trend and price analysis particularly challenging. The stock market is characterized by complexity and volatility, generating vast amounts of data. Among the most commonly used stock data are price-related metrics (e.g., opening price, closing price, high price, and low price) and trading-related metrics (e.g., trading volume and turnover). Price data directly reflect stock trends, while trading dataindirectly indicate trends through market activity and capital dynamics. These datasets are considered fundamental, as they capture diverse information affecting stock price movements from various perspectives [3]. How to deeper mine these stock data and how to use them more effectively are the new challenges. At present, the methods of stock price prediction can be roughly divided into three categories. The first category uses time series models based on statistics and probability theory, the second category uses machine learning-based prediction models, and the third category uses deep learning models. However, most of the methods only use a single model or a series connection of multiple models to predict the future stock trend. Whether using a single model or a cascaded model to enhance robustness, these methods are limited in their ability to extract comprehensive information from the data. The lack of an effective feature extraction directly constrains the model's learning capacity, leading to a suboptimal prediction performance [4; 5]. Ensemble learning (EL), a paradigm that combines multiple learning algorithms to form committees, has demonstrated superior performance compared to individual classifiers or regressors. EL techniques improve predictions through stacking and blending, reduce variance via bagging, and minimize bias through boosting [6; 7]. EL has been successfully applied across various sectors, including healthcare [8], agriculture [9], energy [10], oil and gas [11], and finance [12]. In these applications, ensemble methods consistently achieve higher accuracy than standalone models, supporting their effectiveness. However, the aforementioned methods primarily focus on stock data for mining and prediction. Recently, researchers have begun incorporating text mining techniques to extract sentiment indicators from various media sources, using these as additional features for stock price prediction. Studies have shown that integrating sentiment indices with models, such as the LSTM and SVM, can enhance prediction accuracy [13; 14]. Nevertheless, the inclusion of more features demands stronger data mining capabilities, and effectively analyzing the potential relationships among multi-factor data has become a critical research focus. In this context, we propose a novel multi-variable time series stock prediction framework. This approach leverages deep learning methods to uncover correlations and hidden patterns in multi-variable data across different time intervals. The novelty of our work lies not in the individual components but in their innovative integration to improve overall performance. Specifically, our framework incorporates a variational autoencoder (VAE) step, which enhances robustness to input data, and a neural network structure designed to capture both temporal patterns and relationships among predictive factors. The key contributions of this work are as follows. 1. We propose a novel integrated approach to enhance the robustness of predictions. By applying discrete wavelet transform to data with different sampling frequencies, we effectively reduce noise and minimize correlations within the data.