Predictive Analysis of Tesla's Stock Closing Prices Utilizing LSTM and GRU Deep Learning Models

Yiheng Chi

2024

Abstract

This study delves into advanced deep learning methods, namely Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), to predict Tesla’s stock prices from 2013 to 2023, a period marked by notable market volatility. It aims to analyze these models’ abilities in capturing complex financial trends, particularly in the rapidly evolving electric vehicle sector. The research employs a hybrid approach, combining LSTM and GRU layers to leverage their respective strengths in long-term and short-term forecasting. Methodologically, the study involves comprehensive data processing, model building, and validation using historical stock data from the Nasdaq platform. The models are evaluated through various statistical metrics, including RMSE, MSE, and MAE, to assess their predictive accuracy. The findings reveal that while GRU models excel in short-term forecasting, the hybrid model demonstrates stronger capabilities in long-term trend analysis. This suggests the need for tailored model selection based on specific forecasting timelines in financial markets. The study’s implications extend to the practical application of LSTM and GRU models, recommending an integrated approach for more accurate and responsive market forecasting. It also highlights the potential for future research to incorporate real-time market data, enhancing the models’ relevance and adaptability in a rapidly changing financial landscape.

Paper Citation

in Harvard Style

Chi Y. (2024). Predictive Analysis of Tesla's Stock Closing Prices Utilizing LSTM and GRU Deep Learning Models. In Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-690-3, SciTePress, pages 422-428. DOI: 10.5220/0012807500004547

in Bibtex Style

@conference{icdse24,
author={Yiheng Chi},
title={Predictive Analysis of Tesla's Stock Closing Prices Utilizing LSTM and GRU Deep Learning Models},
booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2024},
pages={422-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012807500004547},
isbn={978-989-758-690-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Predictive Analysis of Tesla's Stock Closing Prices Utilizing LSTM and GRU Deep Learning Models
SN - 978-989-758-690-3
AU - Chi Y.
PY - 2024
SP - 422
EP - 428
DO - 10.5220/0012807500004547
PB - SciTePress