TESLA Stock Prediction Using Machine Learning Models

DOI

10.2991/978-94-6463-823-3_68How to use a DOI?

Keywords

Stock Prediction; Machine Learning; Tesla; Random Forest; Time Series Analysis

Abstract

This paper presents a comprehensive comparative analysis of three machine learning models—Linear Regression, Random Forest, and K-Nearest Neighbors (KNN)—for predicting Tesla stock prices using historical data from 2020 to 2024. The study evaluates model performance based on the Mean Squared Error (MSE), computational efficiency, and robustness to noise. Experimental results demonstrate that the Random Forest model outperforms the others, achieving the lowest test MSE (153.98), attributed to its ensemble learning mechanism and ability to capture non-linear relationships. Linear Regression exhibits moderate performance with a test MSE of 176.46, while KNN lags significantly due to its sensitivity to noise and suboptimal hyperparameter selection, resulting in a test MSE of 229.04. This analysis highlights the critical importance of model selection in financial forecasting, particularly in balancing accuracy, interpretability, and computational resources. The study provides valuable insights for investors and researchers aiming to leverage machine learning techniques in the high-volatility markets.

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  - Suwei Cao
PY  - 2025
DA  - 2025/08/31
TI  - TESLA Stock Prediction Using Machine Learning Models
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 695
EP  - 700
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_68
DO  - 10.2991/978-94-6463-823-3_68
ID  - Cao2025
ER  -