Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction

ArXiv ID: 2411.05790 “View on arXiv”

Authors: Unknown

Abstract

In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical data and identify potential trends. This paper takes AI driven stock price trend prediction as the core research, makes a model training data set of famous Tesla cars from 2015 to 2024, and compares LSTM, GRU, and Transformer Models. The analysis is more consistent with the model of stock trend prediction, and the experimental results show that the accuracy of the LSTM model is 94%. These methods ultimately allow investors to make more informed decisions and gain a clearer insight into market behaviors.

Keywords: LSTM, GRU, Transformer Models, Stock Trend Prediction, Artificial Intelligence, Equities

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders
• Why: The paper uses standard deep learning architectures (LSTM, GRU, Transformer) with minimal novel derivations, placing it in the moderate math range. However, it includes a specific implementation on Tesla stock data (2015-2024) with reported accuracy metrics, indicating practical application and data-driven results.

  flowchart TD
    A["Research Goal: Compare AI Models for Stock Price Prediction"] --> B["Data: Tesla Stock 2015-2024"]
    B --> C["Model Training & Evaluation<br>LSTM vs. GRU vs. Transformer"]
    C --> D["Key Finding: LSTM achieved 94% Accuracy"]
    D --> E["Outcome: Informed Investment Decisions"]