Short-Term Stock Price Forecasting Using ARIMA: A Case Study on Apple and Amazon

Zikai Feng

2025

Abstract

In stock market, predicting stock price has attracted many researchers interest over years because of the non-stationary and highly volatile nature of stock prices. Among various time series forecasting models, the Auto Regressive Integrated Moving Average (ARIMA) model has been widely applied because of its ability to capture the patterns for short term prediction. This study applies the Auto Regressive Integrated Moving Average model to forecast Apple (AAPL) stock value and Amazon (AMZN) stock value. Using the Akaike Information Criterion (AIC) to select the model that fits the best, and ARIMA (0,1,0) is found to be the best option for both stocks. The Root Mean Square Error (RMSE) is used to estimate the accuracy of model forecast. Result of this paper illustrate that ARIMA model exhibits an impressive aptitude for short-term stock price predictions, offering a reference for future research and investment strategies. This study aims to demonstrate the effectiveness of the ARIMA model in short-term stock value predicting, delivering a resource for financial market analysts and financial institutions.

Paper Citation

in Harvard Style

Feng Z. (2025). Short-Term Stock Price Forecasting Using ARIMA: A Case Study on Apple and Amazon. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 366-371. DOI: 10.5220/0013697500004670

in Bibtex Style

@conference{icdse25,
author={Zikai Feng},
title={Short-Term Stock Price Forecasting Using ARIMA: A Case Study on Apple and Amazon},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={366-371},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013697500004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Short-Term Stock Price Forecasting Using ARIMA: A Case Study on Apple and Amazon
SN - 978-989-758-765-8
AU - Feng Z.
PY - 2025
SP - 366
EP - 371
DO - 10.5220/0013697500004670
PB - SciTePress