Work

Prediction of Financial Time-series Data Using ARIMA Model and Machine Learning Algorithm

Year: 2025

Type: conference-paper

Abstract: Stock price prediction is a crucial area in finance, directly influencing investment decisions, market strategies, and the broader economic landscape. Using the stock price of BHP Group Ltd. as a case study, we compare the performance of the ARIMA model and the LSTM network for stock price prediction. For the ARIMA model, we utilize log-return data to predict stock prices. Our results indicate that while the ARIMA model performs reasonably well for short-term predictions, its accuracy diminishes for long-term predictions due to its inherently linear nature. A notable advantage of the ARIMA model is its ability to deliver results without requiring extensive data for training. Conversely, the LSTM algorithm demonstrates higher accuracy, particularly for long-term predictions, though it requires a larger dataset to train effectively.

Source: Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence (DEAI)

DOI: 10.1145/3745238.3745492

Authors: Peng Xia, Jinping Feng, Tianhai Tian

Institutions: Zhongnan University of Economics and Law, Henan University, Monash University

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Funder: National Natural Science Foundation of China

Award: G3434485650