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

ArXiv ID: 2503.22192 “View on arXiv”

Authors: Unknown

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.

Keywords: ensemble deep learning, variational autoencoder (VAE), Transformer, long short-term memory (LSTM), technical indicators, Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced neural network architectures (VAE, Transformer, LSTM) with detailed mathematical formulations for technical indicators and model structures, demonstrating high math complexity. It also includes empirical validation through benchmarking on multiple datasets, comprehensive feature engineering, and performance metrics, indicating strong empirical rigor for implementation.

  flowchart TD
    A["Research Goal<br>Predict Stock Prices with High Reliability"] --> B
    B["Data & Inputs<br>Technical Indicators, Historical Stock Data"] --> C
    subgraph C["Computational Process: Ensemble Framework"]
        C1["VAE Learns Linear Representation"]
        C2["Transformer Identifies Long-Term Patterns"]
        C3["LSTM Captures Temporal Dynamics"]
        C1 & C2 & C3 --> C4["Fusion/Ensemble of Model Outputs"]
    end
    C4 --> D["Key Findings/Outcomes<br>High Accuracy & Reliability<br>Superior to Single Models"]