Advanced Machine Learning Techniques for Accurate Forecasting of Crude Palm Oil Price
DOI:
https://doi.org/10.21009/ISC-BEAM.012.01Keywords:
Crude Palm Oil (CPO); Price Forecasting; Machine Learning; Transformers; Macroeconomic Indicators; Environmental Sustainability.Abstract
The accurate forecasting of crude palm oil (CPO) prices is of paramount importance to stakeholders across the agricultural and financial sectors, as it directly influences critical decisions related to production, trading, and investment strategies. Traditional time series models, while valuable, often fall short in capturing the intricate, non-linear dynamics inherent in CPO price fluctuations. This research delves into the application of cutting-edge machine learning techniques, with a particular emphasis on state-of-the-art models like transformers and hybrid architectures, to significantly enhance the precision of CPO price predictions. This study provides a comprehensive overview of existing research on traditional CPO forecasting methodologies, while also exploring the promising potential of machine learning applications in this domain. By critically analyzing previous studies and highlighting emerging trends, this preliminary investigation aims to establish a benchmark for future research in the field of CPO price prediction. The findings presented herein are intended to serve as a valuable reference point, illuminating the progress made thus far and identifying key areas for further exploration.
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Sahin, O. &. (2021). Machine learning models for predicting CPO prices: A comprehensive review. Renewable and Sustainable Energy Reviews. Oil Palm Industry Economic Journal, 55(3), 145-157.
Salazar, J. &. (2018). Forecasting CPO prices with hybrid ARIMA-ANN models. Energy Policy Journal, 62(1), 98-107.
Shah, S. &. (2020). Hybrid models for accurate CPO price forecasting. Journal of Forecasting, 49(2), 211-223.
Smith, P. &. (2020). Enhancing CPO price prediction using hybrid models. Journal of Agricultural and Food Economics, 45(4), 332-345.
Sun, W. &. (2021). Predicting CPO prices with deep learning models. Journal of Commodity Markets, 51(1), 78-91.
Tan, K. &. (2018). Comparative analysis of ARIMA and LSTM models for CPO price prediction. Journal of Applied Econometrics, 31(3), 273-287.
Vaswani, A. S. (2017). Attention is all you need. Advances in Neural Information Processing Systems. 31st Conference on Neural Information Processing Systems (NIPS 2017), 30, 5998-6008.
Wang, J. &. (2020). Hybrid deep learning models for CPO price forecasting. Journal of Agricultural and Resource Economics, 45(3), 123-145.
Xiao, L. &. (2021). Enhancing CPO price prediction with hybrid models. Journal of Computational Economics, 57(2), 189-201.
Yang, Z. &. (2019). Comparative study of machine learning models for CPO price forecasting. Journal of Commodity Markets, 44(3), 87-101.
Yuan, C. Z. (2020). Long Short-Term Memory Model Based Agriculture Commodity Price. Proceedings of the 2020 2nd International Conference on Information Technology, 43-49.
Zhang, S. &. (2020). Predicting CPO prices with machine learning models: A comprehensive review. Journal of Agricultural Economics, 55(2), 145-169.
Amal, I. (2021). Crude Palm Oil Price Prediction Using Multilayer Perceptron vs Long Short-Term Memory Networks. SCIK Publishing Corporation.
Chen, C. &. (2020). Machine learning approaches for CPO price prediction. International Journal of Economics and Financial Issues, 10(4), 223-230.
Fastmarkets. (2023). Palm oil price and production outlook. Retrieved from Fastmarkets: https://www.fastmarkets.com
Ghosh, S. &. (2019). Comparative analysis of traditional and machine learning models for CPO price forecasting. Journal of Applied Economics, 50(2), 332-349.
Hidayat, A. (2023). Hidayat, A. (2023). Competitiveness, Market Structure, and Energy Analysis of Indonesian CPO. International Journal of Energy Economics and Policy, 151-154.
IISD. (2023). Global Market Report: Palm Oil. International Institute for Sustainable Development. . Retrieved from https://www.iisd.org/system/files/2023-06/2023-global-market-report-palm-oil.pdf
Kanchymalay, K. (2020). A comparative study on univariate time series models and LSTM networks for CPO price prediction. Journal of Advanced Trends in Computers Science and Engineering, 65-67.
Khalid, N. (2018). Crude Palm Oil Price Forecasting in Malaysia: ARDL Model. Jurnal Ekonomi Malaysia.
Kim, H. &. (2018). Forecasting CPO prices using ARIMA and LSTM. Energy Economics Journal, 45(3), 147-157.
Mukkamala, R. e. (2023). A Hybrid Deep Learning Approach for Crude Oil Price Prediction. Journal of Risk and Financial Management , 15-24.
Norlin, K. (2018). Crude Palm Oil Price Forecasting in Malaysia: ARDL Model. Jurnal Ekonomi Malaysia., 22-27.
Ofuoku, M. (2022). Predicting the Price of Crude Palm Oil: A Deep Learning Approach. International Journal of Strategic Decision Sciences, 187-189.
Palm Oil Analytics. (2023). Predicting Palm Oil Prices with Advanced Forecasting Techniques. Retrieved from Palm Oil Analytics: https://www.palmoilanalytics.com
Park, J. &. (2021). Comparative study of machine learning models for CPO price forecasting. Journal of Computational Finance, 39(5), 398-410.
Qi, J. &. (2020). Time series forecasting of CPO prices using deep learning models. Journal of Agricultural Economics, 55(1), 123-135.
Rong, Y. X. (2023). Prediction on Crude Palm Oil Futures (FCPO) Price Using Transformers. International Journal of Academic Research in Business and Social Sciences, 15-25.
Saba, H. &. (2019). Enhancing CPO price prediction with machine learning techniques. Journal of Agricultural and Resource Economics, 41(2), 211-227.
Sahin, O. &. (2021). Machine learning models for predicting CPO prices: A comprehensive review. Renewable and Sustainable Energy Reviews. Oil Palm Industry Economic Journal, 55(3), 145-157.
Salazar, J. &. (2018). Forecasting CPO prices with hybrid ARIMA-ANN models. Energy Policy Journal, 62(1), 98-107.
Shah, S. &. (2020). Hybrid models for accurate CPO price forecasting. Journal of Forecasting, 49(2), 211-223.
Smith, P. &. (2020). Enhancing CPO price prediction using hybrid models. Journal of Agricultural and Food Economics, 45(4), 332-345.
Sun, W. &. (2021). Predicting CPO prices with deep learning models. Journal of Commodity Markets, 51(1), 78-91.
Tan, K. &. (2018). Comparative analysis of ARIMA and LSTM models for CPO price prediction. Journal of Applied Econometrics, 31(3), 273-287.
Vaswani, A. S. (2017). Attention is all you need. Advances in Neural Information Processing Systems. 31st Conference on Neural Information Processing Systems (NIPS 2017), 30, 5998-6008.
Wang, J. &. (2020). Hybrid deep learning models for CPO price forecasting. Journal of Agricultural and Resource Economics, 45(3), 123-145.
Xiao, L. &. (2021). Enhancing CPO price prediction with hybrid models. Journal of Computational Economics, 57(2), 189-201.
Yang, Z. &. (2019). Comparative study of machine learning models for CPO price forecasting. Journal of Commodity Markets, 44(3), 87-101.
Yuan, C. Z. (2020). Long Short-Term Memory Model Based Agriculture Commodity Price. Proceedings of the 2020 2nd International Conference on Information Technology, 43-49.
Zhang, S. &. (2020). Predicting CPO prices with machine learning models: A comprehensive review. Journal of Agricultural Economics, 55(2), 145-169.
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Published
2024-08-01
How to Cite
Ismail, M. A. A., Muhamat, A. A., & Yahya, N. C. (2024). Advanced Machine Learning Techniques for Accurate Forecasting of Crude Palm Oil Price. International Student Conference on Business, Education, Economics, Accounting, and Management (ISC-BEAM), 2(1). https://doi.org/10.21009/ISC-BEAM.012.01
