融合VAE、Transformer与LSTM的深度学习框架，提升股价预测准确性。
An Advanced Ensemble Deep Learning Framework for Stock Price Prediction Using VAE, Transformer, and LSTM Model
- 采用三模型集成：VAE提取线性特征，Transformer捕捉长期模式，LSTM处理时序动态。
- 在多个股票数据集上表现优于单一模型和传统方法，方向预测准确率高且结果波动小。
- 适合量化交易、金融风控与决策支持，具备高可靠性与可扩展性。
本研究提出一种先进的集成深度学习框架，用于股票价格预测，结合变分自编码器（VAE）、Transformer与长短期记忆网络（LSTM）三种先进神经网络架构。该框架旨在充分利用各模型优势，识别股价变动中的线性与非线性关系。通过引入丰富的技术指标，并根据当前市场状况动态调整预测器，显著提升预测精度。在多个股票数据集上的实验表明，该集成方法相较单一模型及传统预测方法，表现出持续的高准确性和可靠性。VAE擅长在高维数据中学习线性表示，Transformer在识别长期股价模式方面表现优异，而基于序列建模能力的LSTM则有效增强对时间动态与波动性的捕捉。三者协同提升了方向预测性能，预测结果差异极小。该方案为解决股价预测固有难题提供了高可靠性与可扩展性的可行路径。相比基于神经网络的独立模型及经典方法，集成框架展现出显著优势，具有重要的算法交易、风险分析与金融决策应用价值。
原文摘要 · Abstract (English)
This research proposes a cutting-edge ensemble deep learning framework for stock price prediction by combining three advanced neural network architectures: The particular areas of interest for the research include but are not limited to: Variational Autoencoder (VAE), Transformer, and Long Short-Term Memory (LSTM) networks. The presented framework is aimed to substantially utilize the advantages of each model which would allow for achieving the identification of both linear and non-linear relations in stock price movements. To improve the accuracy of its predictions it uses rich set of technical indicators and it scales its predictors based on the current market situation. By trying out the framework on several stock data sets, and benchmarking the results against single models and conventional forecasting, the ensemble method exhibits consistently high accuracy and reliability. The VAE is able to learn linear representation on high-dimensional data while the Transformer outstandingly perform in recognizing long-term patterns on the stock price data. LSTM, based on its characteristics of being a model that can deal with sequences, brings additional improvements to the given framework, especially regarding temporal dynamics and fluctuations. Combined, these components provide exceptional directional performance and a very small disparity in the predicted results. The present solution has given a probable concept that can handle the inherent problem of stock price prediction with high reliability and scalability. Compared to the performance of individual proposals based on the neural network, as well as classical methods, the proposed ensemble framework demonstrates the advantages of combining different architectures. It has a very important application in algorithmic trading, risk analysis, and control and decision-making for finance professions and scholars.
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