Price Prediction Using Time-Series Algorithms for Stocks Listed on Bursa Malaysia
Abstract
Investing in stocks markets is risky. It needs a lot of research and time to make the right investment decision for earning lucrative profits. In this study, we attempted to assist the investors in predicting stock prices. We evaluated five time-series algorithms in this study, i.e., Auto-Regression Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Seasonal Auto-Regression Integrated Moving Average (SARIMA), Holt Winter and Prophet on six stocks sectors of Bursa Malaysia. Our findings show that ARIMA and LSTM give relatively low errors in predicting stock prices, and ARIMA is better than LSTM in predicting the trend of the stock prices. The results also suggest that ARIMA is suitable for short-term predictions of stock prices.
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ARIMA and LSTM have comparatively low errors in prediction.
“The results show that ARIMA and LSTM have comparatively low errors in prediction.”
From Intelligent Decision Making for Commodities Price Prediction: Opportunities, Challenges and Future Avenues (2025) · cites this paper as Choy et al. (2021)
Cites
Chai, T., & Draxler, R. R. (2014) · Geoscientific Model Development · doi:10.5194/gmd-7-1247-2014 · Cited by 7,178 · see citing works
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Saeed, N., Shafi, I., Pervez, S., Thompson, E. B., Castilla, A. K., Samad, M. A., & Ashraf, I. (2025) · Computational Economics · doi:10.1007/s10614-024-10837-5 · Cited by 8 · see citing works
- Authors
- Choy, Y.-T., Hoo, M. H., & Khor, K.-C.
- Affiliations
- Universiti Tunku Abdul Rahman Sungai Long Campus (MY)
- ORCID
- Khor, Kok-Chin
- Venue
- 2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS)
- Publisher
- IEEE
- Published
- 2021-09-08
- Pages
- 1-5
- OpenAlex
- W3209966054
- Cited by
- 17
- References
- 1
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 6 · 2023: 5 · 2022: 3
- FWCI
- 5.10
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Artificial Intelligence and Data Sciences (AiDAS) (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- stock price prediction, time series forecasting, ARIMA, LSTM, SARIMA, Holt-Winters, prophets, Bursa Malaysia, short-term forecasting, stock investment
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