An Advanced Ensemble Deep Learning Framework for Stock Price Prediction Using VAE, Transformer, and LSTM Model

2025-03-28

AI Summary

This paper addresses the insufficient modeling of both linear and nonlinear dynamics in stock price prediction. We propose a VAE-Transformer-LSTM collaborative ensemble framework: a Variational Autoencoder (VAE) extracts low-dimensional, robust latent representations to capture linear structures; a Transformer models long-range dependencies; and an LSTM encodes sequential temporal dynamics. To enhance generalizability and market adaptability, we introduce a market-adaptive feature scaling mechanism and a multi-source technical indicator fusion strategy. Extensive experiments across multiple stock datasets demonstrate that our method significantly outperforms individual models and conventional benchmarks—achieving a 12.3% improvement in directional accuracy and an 18.7% reduction in mean absolute error (MAE). The framework exhibits strong practical potential for algorithmic trading and risk management applications.

Technology Category

• Machine Learning: Ensemble Methods
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Predicts stock prices using VAE, Transformer, and LSTM ensemble
• Identifies linear and non-linear stock price movement patterns
• Improves accuracy with technical indicators and market-scaled predictors

Innovation

• Combines VAE, Transformer, LSTM for stock prediction
• Uses technical indicators and market-scaled predictors
• Ensemble framework outperforms single models