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

TL;DR

This paper compares LSTM, GRU, and Transformer models for stock price prediction using Tesla data from 2015 to 2024, finding LSTM achieves the highest accuracy of 94%, aiding investors in decision-making.

Contribution

It provides a comparative analysis of LSTM, GRU, and Transformer models specifically for stock trend prediction with a new dataset of Tesla stock data.

Findings

LSTM achieved 94% prediction accuracy.

Transformer models performed comparably but slightly less accurately.

The study offers insights into model selection for stock prediction tasks.

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.