Predicting Tesla’s Stock Price with LSTM

DOI

10.2991/978-94-6463-823-3_62How to use a DOI?

Keywords

Long- and Short-Term Memory; Tesla; Machine Learning; Deep Learning; Price Prediction

Abstract

Stock price prediction is an important topic in market analysis, and accurate prediction can support investor’s investment decisions. Because of its benefits in processing time series data, deep learning techniques—particularly the Long Short-Term Memory (LSTM) technique—have been increasingly popular in recent years for stock price prediction. This study’s goal is to use the LSTM model to forecast Tesla’s stock price. The Tesla stock data from 2010–2025, which was made available by Kaggle, served as the primary dataset for this investigation. This article first normalizes the data. Subsequently, this article designed an LSTM-based deep learning model, which includes two LSTM layers, two culling layers, and a fully connected layer, and optimally trained it using the training set. This article employed metrics like MSE, RMSE, and MAE to assess the model’s performance and contrasted it with more conventional models. The experimental results show that the LSTM model can predict Tesla’s stock price successfully and perform better than the traditional model in terms of prediction accuracy.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yizheng Wang
PY  - 2025
DA  - 2025/08/31
TI  - Predicting Tesla’s Stock Price with LSTM
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 626
EP  - 633
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_62
DO  - 10.2991/978-94-6463-823-3_62
ID  - Wang2025
ER  -