1. Home
  2. Vol 8, No 3 (2024)
  3. Tardini

Selection of Modelling for Forecasting Crude Palm Oil Prices Using Deep Learning (GRU & LSTM)

Gabriella Alexander Tardini, . Suharjito

Abstract


The unstable crude palm oil (CPO) prices have an impact on assessments of economic growth and environmental sustainability, as well as market strategies, international trade discussions, and consumer pricing expectations for products made from CPO. Therefore, it is crucial to identify the best prediction method to accurately forecast this cost. This research aims to develop an accurate time series data prediction model for crude palm oil prices using GRU and LSTM methods. The study also aims to identify the best-performing model by comparing their performance. This study uses LSTM and GRU methods as well as Bi-LSTM as a comparison, using crude palm oil price data from the Indonesian Ministry of Trade (August 1, 2018–August 31, 2023) from Medan (1064 data points) and Rotterdam SPOT (617 data), Rotterdam Forward 1 Month (1,022 data), and Rotterdam Forward 2 Month (950 data). Each dataset is then split into training and testing data with a 70:30 ratio. The hyperparameters used are (a) learning rate: (0.001); (b) batch size: 100; (c) node: (512); (d) optimizer: Adam; (e) and epoch: 50. The results of the forecast are highly accurate with a MAPE of less than 10%. Overall, both LSTM and GRU techniques demonstrate excellent performance in forecasting crude palm oil prices, but they may need to be modified based on data features. The ARIMA method can also be considered for forecasting. Future studies may consider optimizing parameters and model structures.

 

Doi: 10.28991/ESJ-2024-08-03-05

Full Text: PDF


Keywords


Adam; Crude Palm Oil; Deep Learning; Forecast; Price Prediction.

References


FAS. (2024). Production-Palm Oil. U. S. Department of Agriculture, Foreign Agriculture Service, Washington, United States. Available online: https://fas.usda.gov/data/production/commodity/4243000 (accessed on May 2024).

Ayompe, L. M., Schaafsma, M., & Egoh, B. N. (2021). Towards sustainable palm oil production: The positive and negative impacts on ecosystem services and human wellbeing. Journal of Cleaner Production, 278, 123914. doi:10.1016/j.jclepro.2020.123914.

Achaw, O.-W., & Danso-Boateng, E. (2021). Chemical and Process Industries. Springer International Publishing, Cham, Switzerland. doi:10.1007/978-3-030-79139-1.

Tanwar, B., & Goyal, A. (2021). Oilseeds: Health Attributes and Food Applications. Springer, Singapore. doi:10.1007/978-981-15-4194-0.

Al-Sabaeei, A. M., Al-Fakih, A., Noura, S., Yaghoubi, E., Alaloul, W., Al-Mansob, R. A., Imran Khan, M., & Aliyu Yaro, N. S. (2022). Utilization of palm oil and its by-products in bio-asphalt and bio-concrete mixtures: A review. Construction and Building Materials, 337, 127552. doi:10.1016/j.conbuildmat.2022.127552.

Lim, C. H., Lim, S., How, B. S., Ng, W. P. Q., Ngan, S. L., Leong, W. D., & Lam, H. L. (2021). A review of industry 4.0 revolution potential in a sustainable and renewable palm oil industry: HAZOP approach. Renewable and Sustainable Energy Reviews, 135(February 2020), 110223. doi:10.1016/j.rser.2020.110223.

Amin, F. A. M., Sukeri, N. A. S. M., Hasbullah, N., & Jamaludin, N. (2019). Forecasting Value at Risk for Malaysian Palm Oil using Monte Carlo Simulation. International Journal of Academic Research in Business and Social Sciences, 9(9), 1253–1260. doi:10.6007/ijarbss/v9-i9/6418.

Helbawanti, O., Permata Bunda, C. A., & Faqihuddin, F. (2022). Integrasi Pasar Berjangka Komoditi Crude Palm Oil (CPO) Indonesia Di Masa Pandemi Covid-19. Jurnal Agrica, 15(1), 61–74. doi:10.31289/agrica.v15i1.5401.

Siregar, K., & Syafarudin, A. (2020). Analysis of Crude Palm Oil (CPO) Production Volume and Price on Profitability and Its Impact on Stock Returns. International Journal of Engineering Technologies and Management Research, 6(7), 87–100. doi:10.29121/ijetmr.v6.i7.2019.419.

Liu, Y., Wang, Z., & Zheng, B. (2019). Application of Regularized GRU-LSTM Model in Stock Price Prediction. 2019 IEEE 5th International Conference on Computer and Communications (ICCC), Chengdu, China. doi:10.1109/iccc47050.2019.9064035.

Aisyah, S., Ulinnuha, N., & Hamid, A. (2023). Application of Extreme Learning Machine in Forecasting Crude Palm Oil Prices. KUBIK: Jurnal Publikasi Ilmiah Matematika, 7(2), 97–105. doi:10.15575/kubik.v7i2.20460. (In Indonesian).

Amal, I. (2021). Crude palm oil price prediction using multilayer perceptron and long short-term memory. Journal of Mathematical and Computational Science. doi:10.28919/jmcs/6680.

Saadah, S., Fakhira, Z.Z., & Hasna, H.Z. (2021). Support Vector Machine (SVM) To Predict Crude Oil Palm in Indonesia and Exchange Rate of EUR/USD. Journal of Computer Science and Informatics Engineering (J-Cosine), 5(1), 85–92. doi:10.29303/jcosine.v5i1.403.

Wei, K.Y., & Banu Samsudin, H. (2021). Comparison between Artificial Neural Network and ARIMA Model in Forecasting Palm Oil Price in Malaysia. International Journal of Scientific Engineering and Science, 5(11), 12–15.

Shehu, U. E., Chow, T. Q., Hafid, H. S., Mokhtar, M. N., Baharuddin, A. S., & Nawi, N. M. (2019). Kinetics of thermal hydrolysis of crude palm oil with mass and heat transfer in a closed system. Food and Bioproducts Processing, 118, 187–197. doi:10.1016/j.fbp.2019.09.009.

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts, Melbourne, Australia.

Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). The Performance of LSTM and BiLSTM in Forecasting Time Series. 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, United States. doi:10.1109/bigdata47090.2019.9005997.

ArunKumar, K. E., Kalaga, D. V., Mohan Sai Kumar, C., Kawaji, M., & Brenza, T. M. (2022). Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends. Alexandria Engineering Journal, 61(10), 7585–7603. doi:10.1016/j.aej.2022.01.011.

Yamak, P. T., Yujian, L., & Gadosey, P. K. (2019). A comparison between ARIMA, LSTM, and GRU for time series forecasting. ACM International Conference Proceeding Series, 49–55. doi:10.1145/3377713.3377722.

Luceño, A., & Peña, D. (2007). Autoregressive Integrated Moving Average (ARIMA) Modeling. Encyclopedia of Statistics in Quality and Reliability, 2. doi:10.1002/9780470061572.eqr276.

Liu, T., Liu, S., & Shi, L. (2020). ARIMA Modelling and Forecasting. Springer Briefs in Statistics, 61–85, Springer, Singapore. doi:10.1007/978-981-15-0321-4_4.

Alway, A., Zamri, N. E., Kasihmuddin, M. S. M., Asyraf Mansor, M., & Sathasivam, S. (2020). Palm oil trend analysis via logic mining with discrete hopfield neural network. Pertanika Journal of Science and Technology, 28(3), 967–981.

Aulia, A. A., Elhanafi, A. M., Dafitri, H., Aulia, A., Elhanafi, A. M., & Dafitri, H. (2021, October). Implementation of the Gated Recurrent Unit Algorithm in Predicting Palm Oil Prices Using the Recurrent Neural Network (RNN) Model. Prosiding SNASTIKOM: Seminar Nasional Teknologi Informasi & Komunikasi Paper, Indonesia.

Chai, L., Xu, H., Luo, Z., & Li, S. (2020). A multi-source heterogeneous data analytic method for future price fluctuation prediction. Neurocomputing, 418, 11–20. doi:10.1016/j.neucom.2020.07.073.

Wang, J., Niu, T., Du, P., & Yang, W. (2020). Ensemble probabilistic prediction approach for modeling uncertainty in crude oil price. Applied Soft Computing Journal, 95. doi:10.1016/j.asoc.2020.106509.

Lee, J. Y., Nguyen, T. T., Nguyen, H. G., & Lee, J. Y. (2022). Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe. Energies, 15(11). doi:10.3390/en15114003.

Sen, A. (2021). An Analysis of Crude Oil Prices in the Last Decade (2011-2020): With Deep Learning Approach. SSRN Electronic Journal. doi:10.2139/ssrn.3906517.

Suryani, D., Fadhilla, M. & Labellapansa, A. (2022). Indonesian Crude Oil Price (ICP) Prediction Using Multiple Linear Regression Algorithm. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(6), 1057–1063. doi:10.29207/resti.v6i6.4590.

Çolakoğlu, N., & Akkaya, B. (2019). Comparison of multi-class classification algorithms on early diagnosis of heart diseases. y-BIS 2019 Conference Book: Recent Advances in Data Science and Business Analytics, 25-28 September, 2019, Istanbul, Turkey.

Solano Meza, J. K., Orjuela Yepes, D., Rodrigo-Ilarri, J., & Cassiraga, E. (2019). Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon, 5(11), 2810. doi:10.1016/j.heliyon.2019.e02810.

Rahman, M., & Raju, V. (2020). Patients’perceptions on Factors Determining of Total Quality Management (TQM) Implementation into Bangladeshi Public Hospitals Using Technology: An Empirical Study. PalArch's Journal of Archaeology of Egypt/Egyptology, 17(7), 6258-6272.

Lawi, A., Mesra, H., & Amir, S. (2022). Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately. Journal of Big Data, 9(1), 89. doi:10.1186/s40537-022-00597-0.

Andrea, I. P., Sitepu, B., & Simatupang, P. (2018). Analysis the Influence of Total Quality Management (TQM) and Joint Commission International (JCI) Toward Patient Safety Program A Case Study: Omni Hospital Group. Master Thesis, Swiss German University, Tangerang, Indonesia.

Ofuoku, M., & Ngniatedema, T. (2022). Predicting the Price of Crude Palm Oil. International Journal of Strategic Decision Sciences, 13(1), 1–15. doi:10.4018/ijsds.305830.

Shahid, F., Zameer, A., & Muneeb, M. (2021). A novel genetic LSTM model for wind power forecast. Energy, 223, 120069. doi:10.1016/j.energy.2021.120069.

Wang, Y., Liu, P., Zhu, K., Liu, L., Zhang, Y., & Xu, G. (2022). A Garlic-Price-Prediction Approach Based on Combined LSTM and GARCH-Family Model. Applied Sciences (Switzerland), 12(22), 11366. doi:10.3390/app122211366.

Namasudra, S., Dhamodharavadhani, S., & Rathipriya, R. (2023). Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases. Neural Processing Letters, 55(1), 171–191. doi:10.1007/s11063-021-10495-w.

Ghane, E., Fagerström, M., & Mirkhalaf, M. (2023). Recurrent neural networks and transfer learning for elasto-plasticity in woven composites. arXiv preprint arXiv:2311.13434. doi:10.48550/arXiv.2311.13434.

Weigelt, S., Steurer, V., Hey, T., & Tichy, W. F. (2020). Roger that! Learning How Laypersons Teach New Functions to Intelligent Systems. 2020 IEEE 14th International Conference on Semantic Computing (ICSC). doi:10.1109/icsc.2020.00020.

Setiawan, K. E., Elwirehardja, G. N., & Pardamean, B. (2023). Indoor Climate Prediction Using Attention-Based Sequence-to-Sequence Neural Network. Civil Engineering Journal, 9(5), 1105-1120. doi:10.28991/CEJ-2023-09-05-06.

Hamayel, M. J., & Owda, A. Y. (2021). A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms. AI (Switzerland), 2(4), 477–496. doi:10.3390/ai2040030.

Busari, G. A., & Lim, D. H. (2021). Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance. Computers & Chemical Engineering, 155, 107513. doi:10.1016/j.compchemeng.2021.107513.

Ubrani, A., & Motwani, S. (2019). LSTM- and GRU-based time series models for market clearing price forecasting of Indian deregulated electricity markets. Advances in Intelligent Systems and Computing, 898, 693–700. doi:10.1007/978-981-13-3393-4_70.

Iaousse, M., Jouilil, Y., Bouincha, M., & Mentagui, D. (2023). A Comparative Simulation Study of Classical and Machine Learning Techniques for Forecasting Time Series Data. International Journal of Online and Biomedical Engineering, 19(8), 56–65. doi:10.3991/ijoe.v19i08.39853.

Wu, H., Chen, S., & Ding, Y. (2023). Comparison of ARIMA and LSTM for Stock Price Prediction. Financial Engineering and Risk Management, 6(1), 1–7. doi:10.23977/ferm.2023.060101.

Atique, S., Noureen, S., Roy, V., Subburaj, V., Bayne, S., & MacFie, J. (2019). Forecasting of total daily solar energy generation using ARIMA: A case study. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019, 114–119. doi:10.1109/CCWC.2019.8666481.

Moitra, N., Raj, P., Saxena, S., & Kumar, R. (2020). Crude oil prediction using LSTM. International Journal of Innovative Science and Research Technology, 5(2), 900-905.

Al-Khowarizmi, Nasution, I. R., Lubis, M., & Lubis, A. R. (2020). The effect of a secos in crude palm oil forecasting to improve business intelligence. Bulletin of Electrical Engineering and Informatics, 9(4), 1604–1611. doi:10.11591/eei.v9i4.2388.

Kanchymalay, K., Salim, N., & Krishnan, R. (2019). Time series based crude palm oil price forecasting model with weather elements using LSTM network. International Journal of Engineering and Advanced Technology, 9(1), 3188–3192. doi:10.35940/ijeat.A9994.109119.


Full Text: PDF

DOI: 10.28991/ESJ-2024-08-03-05

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Gabriella Alexander Tardini, Suharjito Suharjito