Research on Stock Price Prediction Based on the ARIMA Model

Ziyi Yang

2025

Abstract

In the financial field, stock prices have always been of great concern. Accurately and effectively predicting stock prices is beneficial for investors to make reasonable decisions and avoid risks. The autoregressive integrated moving average (ARIMA) model can effectively capture the fluctuation trend of historical stock prices. This paper constructs an ARIMA model to predict the closing price of Zomato on that day. The results show that compared with the real value, the root mean square error (RMSE) value predicted by the ARIMA model is relatively small, only 4.6172, reflecting that the ARIMA model has high accuracy in short-term forecasting (STF). In the long-term forecast, other non-linear factors should be considered, and other models should be combined to make improvements and optimizations to improve the accuracy of the forecast. This research will benefit both providing an effective reference for investors in short-term stock price forecasting and further improvement and perfection of stock price forecasting in the future.

Paper Citation

in Harvard Style

Yang Z. (2025). Research on Stock Price Prediction 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 374-378. DOI: 10.5220/0013826100004708

in Bibtex Style

@conference{iampa25,
author={Ziyi Yang},
title={Research on Stock Price Prediction 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={374-378},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013826100004708},
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 - Research on Stock Price Prediction Based on the ARIMA Model
SN - 978-989-758-774-0
AU - Yang Z.
PY - 2025
SP - 374
EP - 378
DO - 10.5220/0013826100004708
PB - SciTePress