Volume 12, Issue 9 (September 2025), Pages: 169-179

----------------------------------------------

 Original Research Paper

Modeling and forecasting Malaysian rice production: Insights from ARIMA, Exponential Smoothing, and LSTM models

 Author(s): 

 Nur Amalina Shafie *, Ain Raziha Rosli, Wan Nur Syuhada Wan Azmi

 Affiliation(s):

 College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Cawangan Negeri Sembilan, Kampus Seremban, Negeri Sembilan 72000, Malaysia

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0003-4522-1758

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.09.016

 Abstract

This study aims to forecast future rice production in Malaysia concerning national targets by comparing the effectiveness of three models: Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing, and Long Short-Term Memory (LSTM). Unlike ARIMA and Exponential Smoothing, which are based on predefined statistical assumptions, LSTM uses deep learning to detect complex, non-linear, and long-term patterns in time series data. The performance of these models, applied to Malaysia’s annual rice production data from 1960 to 2023, was evaluated using error measures such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results showed that Double Exponential Smoothing produced the lowest error rates, making it the most accurate method for predicting rice production. While LSTM is considered a more advanced technique, it did not perform better than Double Exponential Smoothing in this case. The study concludes that predicted rice production levels are likely to fall below government targets over the next five years. This finding emphasizes the need to focus on sustainability strategies, such as reducing reliance on imports and enhancing domestic rice production. The results can guide policymakers in addressing future challenges, promoting sustainable agricultural practices, and ensuring Malaysia's long-term food security. Future research could explore using hybrid models in a multivariate setting and expanding datasets to compare regional and global rice production trends.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Rice production, Forecasting models, Agricultural practices, Food security, Sustainability strategies

 Article history

 Received 24 October 2024, Received in revised form 22 March 2025, Accepted 17 August 2025

 Acknowledgment

We would like to thank Universiti Teknologi MARA for their financial support through (600-RMC/GPM LPHD 5/3 (136/2021)). 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Shafie NA, Rosli AR, and Azmi WNSW (2025). Modeling and forecasting Malaysian rice production: Insights from ARIMA, Exponential Smoothing, and LSTM models. International Journal of Advanced and Applied Sciences, 12(9): 169-179

  Permanent Link to this page

 Figures

  Fig. 1  Fig. 2  Fig. 3  Fig. 4  Fig. 5  Fig. 6  Fig. 7  Fig. 8 

 Tables

  Table 1  Table 2  Table 3

----------------------------------------------   

 References (17)

1. Abidin Z, Destiarni RP, and Septya F (2018). Reviewing government policy on rice price: Study through rice price in East Java market. In the Proceedings of the International Conference on Science and Technology (ICST 2018), Atlantis Press, Kuala Lumpur, Malaysia: 614-619.  https://doi.org/10.2991/icst-18.2018.126    [Google Scholar]
2. Adiba JIQ, Slamet J, Teguh AM, Septiyanto AF, and Sarno R (2024). Advanced deep learning and hybrid models for forecasting rice production in Indonesia. In the 11th International Conference on Information Technology, Computer, and Electrical Engineering, IEEE, Semarang, Indonesia: 302-307.  https://doi.org/10.1109/ICITACEE62763.2024.10762812    [Google Scholar]
3. Annamalai N and Johnson A (2023). Analysis and forecasting of area under cultivation of rice in India: Univariate time series approach. SN Computer Science, 4: 193.  https://doi.org/10.1007/s42979-022-01604-0    [Google Scholar] PMid:36778724 PMCid:PMC9897162
4. Banerjee T, Sinha S, and Choudhury P (2022). Long term and short term forecasting of horticultural produce based on the LSTM network model. Applied Intelligence, 52: 9117–9147.  https://doi.org/10.1007/s10489-021-02845-x    [Google Scholar]
5. Bezabih G, Wale M, Satheesh N, Fanta SW, and Atlabachew M (2023). Forecasting cereal crops production using time series analysis in Ethiopia. Journal of the Saudi Society of Agricultural Sciences, 22(8): 546-559.  https://doi.org/10.1016/j.jssas.2023.07.001    [Google Scholar]
6. Dorairaj D and Govender NT (2023). Rice and paddy industry in Malaysia: Governance and policies, research trends, technology adoption and resilience. Frontiers in Sustainable Food Systems, 7: 1093605.  https://doi.org/10.3389/fsufs.2023.1093605    [Google Scholar]
7. Habib-ur-Rahman M, Ahmad A, Raza A, Hasnain MU et al. (2022). Impact of climate change on agricultural production; issues, challenges, and opportunities in Asia. Frontiers in Plant Science, 13: 925548.  https://doi.org/10.3389/fpls.2022.925548    [Google Scholar] PMid:36325567 PMCid:PMC9621323
8. Lazim MA (2000). Introductory business forecasting: A practical approach. Univision Press, Los Angeles, USA.    [Google Scholar]
9. Mgale YJ, Yan Y, and Timothy S (2021). A comparative study of Arima and holt-winters exponential smoothing models for rice price forecasting in Tanzania. Open Access Library Journal, 8(5): 1-9.  https://doi.org/10.4236/oalib.1107381    [Google Scholar]
10. Nurviana N, Amelia A, Sari RPSRP, Nabilla UNU, and Talib T (2022). Forecasting rice paddy production in Aceh using Arima and exponential smoothing models. CAUCHY: Jurnal Matematika Murni dan Aplikasi, 7(2): 281-292.  https://doi.org/10.18860/ca.v7i2.13701    [Google Scholar]
11. Saini U, Kumar R, Jain V, and Krishnajith MU (2020). Univariant time series forecasting of agriculture load by using LSTM and GRU RNNS. In the IEEE Students Conference on Engineering and Systems, IEEE, Prayagraj, India: 1-6.  https://doi.org/10.1109/SCES50439.2020.9236695    [Google Scholar]
12. Sassi M, Ali Y, and Ali I (2024). Climate change perception and adaptation among farmers in the mountains of western Karakoram, Pakistan. Development in Practice, 35(1): 3–20.  https://doi.org/10.1080/09614524.2024.2417249    [Google Scholar]
13. Sharma A, Kumar J, Redhu M, Kumar P, Godara M, Ghiyal P, Fu P, and Rahimi M (2024). Estimation of rice yield using multivariate analysis techniques based on meteorological parameters. Scientific Reports, 14: 12626.  https://doi.org/10.1038/s41598-024-63596-6    [Google Scholar] PMid:38824223 PMCid:PMC11637069
14. Staudemeyer RC and Morris ER (2019). Understanding LSTM–A tutorial into long short-term memory recurrent neural networks. Arxiv Preprint Arxiv:1909.09586.  https://doi.org/10.48550/arXiv.1909.09586    [Google Scholar]
15. Stetter C and Cronauer C (2024). Climate and soil conditions shape farmers’ climate change adaptation preferences. Agricultural Economics, 56: 165–187.  https://doi.org/10.1111/agec.12870    [Google Scholar]
16. Wang J, Si H, Gao Z, and Shi L (2022). Winter wheat yield prediction using an LSTM model from MODIS LAI products. Agriculture, 12(10): 1707.  https://doi.org/10.3390/agriculture12101707    [Google Scholar]
17. Yasmin S and Moniruzzaman M (2024). Forecasting of area, production, and yield of jute in Bangladesh using Box-Jenkins ARIMA model. Journal of Agriculture and Food Research, 16: 101203.  https://doi.org/10.1016/j.jafr.2024.101203    [Google Scholar]