A Study of Machine Learning for Crude Palm Oil Price Prediction

Main Article Content

Kanya Siripirom
Datchakorn Tancharoen


This paper focuses on the prediction of Crude Palm Oil prices in Thailand and find the best model for price prediction. Accurate price forecasting is essential for stakeholders in the palm oil industry to make informed decisions and manage risks effectively. In this comparative study between the Baseline Model, Recurrent Neural Network-Long Short-Term Memory, and the Classic models. The Classic model are Regression Model, Exponential Smoothing, Random Forest, LightGBM Model, and Theta Method. The testing will be conducted by comparing performance of models for the crude palm oil price prediction using time series and this is only one factor in this experiment. For the evaluation using the Mean Absolute Percentage Error (MAPE) metric to measure the percentage deviation between the projected and actual prices. It provides insights into the performance of the model. This research found that the baseline model has the lowest MAPE value is 0.08128. Therefore, the Baseline model provides the best accuracy for forecasting crude palm oil prices in Thailand.

Article Details

How to Cite
K. Siripirom and D. Tancharoen, “A Study of Machine Learning for Crude Palm Oil Price Prediction”, JIST, vol. 13, no. 2, pp. 1–12, Dec. 2023.
Research Article: Information Systems (Detail in Scope of Journal)


Kanchymalay, Kasturi, et al. "Multivariate time series forecasting of crude palm oil price using machine learning techniques." IOP Conference Series: Materials Science and Engineering. Vol. 226. No. 1. IOP Publishing, 2017.

Rahim, Nur Fazliana, Mahmod Othman, and Rajalingam Sokkalingam. "A comparative review on various method of forecasting crude palm oil prices." Journal of Physics: Conference Series. Vol. 1123. No. 1. IOP Publishing, 2018.

Bristone, Makumbonori, Rajesh Prasad, and Adamu Ali Abubakar. "CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms." Petroleum 6.4 (2020): 353-361.

Gupta, Varun, and Ankit Pandey. "Crude oil price prediction using LSTM networks." International Journal of Computer and Information Engineering 12.3 (2018): 226-230.

Zhao, Yang, Jianping Li, and Lean Yu. "A deep learning ensemble approach for crude oil price forecasting." Energy Economics 66 (2017): 9-16.

Li, Zhanke, et al. "Oil Price Forecasting Based on Variational Mode Decomposition, Relative Entropy and LSTM Neural Network." IOP Conference Series: Materials Science and Engineering. Vol. 750. No. 1. IOP Publishing, 2020.

Güleryüz, Didem, and Erdemalp Özden. "The prediction of Brent crude oil trend using LSTM and Facebook Prophet." Avrupa Bilim ve Teknoloji Dergisi 20 (2020): 1-9.

Xie, Wen, et al. "A new method for crude oil price forecasting based on support vector machines." Computational Science–ICCS 2006: 6th International Conference, Reading, UK, May 28-31, 2006, Proceedings, Part IV 6. Springer Berlin Heidelberg, 2006.

Khalid, Norlin, et al. "Crude palm oil price forecasting in Malaysia: An econometric approach." Jurnal Ekonomi Malaysia 52.3 (2018): 263-278.

Siew, Han Lock, and Md Jan Nordin. "Regression techniques for the prediction of stock price trend." 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE). IEEE, 2012.

Kim, Jeonghyeon, et al. "A comparative study of machine learning and spatial interpolation methods for predicting house prices." Sustainability 14.15 (2022): 9056.

Guo, Yuankai, Yangyang Li, and Yuan Xu. "Study on the application of LSTM-LightGBM Model in stock rise and fall prediction." MATEC Web of Conferences. Vol. 336. EDP Sciences, 2021.

Hossain, Mohammad Raquibul, and Mohd Tahir Ismail. "Empirical mode decomposition based on theta method for forecasting daily stock price." Journal of Information and Communication Technology 19.4 (2020): 533-558.

Herzen, Julien, et al. "Darts: User-friendly modern machine learning for time series." The Journal of Machine Learning Research 23.1 (2022): 5442-5447.

Bhattacharjee, Indronil, and Pryonti Bhattacharja. "Stock price prediction: a comparative study between traditional statistical approach and machine learning approach." 2019 4th international conference on electrical information and communication technology (EICT). IEEE, 2019.

Mbah, Tawum Juvert, et al. "Using LSTM and ARIMA to simulate and predict limestone Price variations." Mining, Metallurgy & Exploration 38 (2021): 913-926.

Ebenesh, C., and K. Anitha. "A Novel Approach to Minimize the Mean Square Error in Predicting Stock Price Index using Linear Regression in Comparison with LSTM Model." 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2022.