Analysis and Prediction of TESLA Stock Based on the ARIMA Model

Jinghan Wang

2025

Abstract

TESLA, as the leader in new energy vehicles, has set a record high in multiple transaction volumes and profits in 2024. Accurate prediction of Tesla's stock prices is crucial for investors and market analysis. This article will select TESLA's stock price in the past decade to conduct a series of time series analyses such as stationarity test, difference, and residual test to build a suitable Autoregressive Integrated Moving Average (ARIMA) model and use this to predict the stock price in the next five years, hoping to provide strong decision-making support for the government, enterprises, and individuals to promote high-quality development of the new energy vehicles industry. The final results show that the model has a relatively high performance and shows an upward trend. Next, the possible reasons for this phenomenon were further analyzed. However, because stock prices are affected by many factors, there may be large errors in making predictions, and further, updating the model and in-depth research is needed.

Paper Citation

in Harvard Style

Wang J. (2025). Analysis and Prediction of TESLA Stock Based on the ARIMA Model. In Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA; ISBN 978-989-758-774-0, SciTePress, pages 37-41. DOI: 10.5220/0013813600004708

in Bibtex Style

@conference{iampa25,
author={Jinghan Wang},
title={Analysis and Prediction of TESLA Stock Based on the ARIMA Model},
booktitle={Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA},
year={2025},
pages={37-41},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013813600004708},
isbn={978-989-758-774-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Innovations in Applied Mathematics, Physics, and Astronomy - Volume 1: IAMPA
TI - Analysis and Prediction of TESLA Stock Based on the ARIMA Model
SN - 978-989-758-774-0
AU - Wang J.
PY - 2025
SP - 37
EP - 41
DO - 10.5220/0013813600004708
PB - SciTePress