Crude Palm Oil Price Prediction Using Simulated Annealing-based Support Vector Regression (SA-SVR)

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Chai Wen Goh
Jack Chai
Amirah Rahman
Wen Eng Ong


Palm oil is one of the major export products of Malaysia. Predicting the price of crude palm oil futures (FCPO) traded on BURSA Malaysia Derivative is essential as agricultural markets have an inherent tendency towards instability, and thus are more vulnerable to price shocks than other industrial sectors. Hence, if the price of the futures contract on crude palm oil can be forecasted accurately, many parties such as farmers, refiners and distributors can manage the risk of price fluctuations through FCPO. This study proposes the metaheuristic and machine learning hybridised model of simulated annealing-based support vector regression (SA-SVR). The SVR in this model produces close price predictions of the FCPO with minimum deviation from the actual value with the help of SA, which first determines the best hyperparameter set to be utilised in the SVR. Although the proposed Radial Basis Function (RBF) kernelised SA-SVR model inputs only 10% of training data due to memory overload issues, it has produced a satisfying prediction result with an average execution time of 2 minutes and 34 seconds. The model performance was analysed further by using different ratios in data splitting, varying temperature combinations for the SA algorithm and initiating the parameter search based on the previous best hyperparameter set. Results show that keeping the test size constant and extracting more historical data on FCPO price for model training is better than varying train-test split ratios. The temperature schedule strategy showed that different initial and minimum SA temperature combinations affects the overall optimisation results. The best combination was the initial temperature of 100 and minimum of 40. In addition, the number of temperature reductions and average execution time to reach the best state decreases when the starting point of the parameter search space is close to the best values.

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Crude Palm Oil Price Prediction Using Simulated Annealing-based Support Vector Regression (SA-SVR). (2024). Asian Academy of Management Journal of Accounting and Finance, 20(1), 305-333.


Aini, H., & Haviluddin, H. (2019). Crude palm oil prediction based on backpropagation neural network approach. Knowledge Engineering and Data Science, 2, 1–9.

Alahmari, S. A. (2020). Predicting the price of cryptocurrency using support vector regression methods. Journal of Mechanics of Continua and Mathematical Sciences, 15(4), 313–322.

Almassian, N., Azmi, R., & Berenji, S. (2009). AIDSLK: An anomaly based intrusion detection system in Linux Kernel. Communications in Computer and Information Science, 31, 232–243.

Alwadi, S., Almasarweh, M., & Alsaraireh, A. (2018). Predicting closed price time series data using ARIMA model. Modern Applied Science, 12, 181.

Amal, I., Tarno, & Suparti. (2021). Crude palm oil price prediction using multilayer perceptron and long short-term memory. Journal of Mathematics and Computer Science, 11(6), 8034–8045.

Arshad, F. M., & Ghaffar, R. A. (1986). Crude palm oil price forecasting: Box-Jenkins approach. Pertanika Journal of Tropical Agricultural Science, 9(3), 359–367.

Awad, M., & Khanna, R. (2015). Support vector regression. In M. Awad, & R. Khanna (Eds.), Efficient learning machines: theories, concepts, and applications for engineers and system designers (pp. 67–80). Springer Nature.

BURSA Malaysia. (2021). Crude palm oil futures (FCPO).

Dong, Y., Li, S., & Gong, X. (2017, January). Time series analysis: An application of ARIMA model in stock price forecasting. In Proceedings of the 2017 International Conference on Innovations in Economic Management and Social Science (IEMSS 2017) (pp. 2352–5428). Atlantis Press.

Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1996). Support vector regression machines. In M. C. Mozer, M. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems (vol. 9, pp. 155–161). MIT Press.

Fischetti, M., & Stringher, M. (2019). Embedded hyper-parameter tuning by simulated annealing. ArXiv.

Funde, Y., & Damani, A. (2023). Comparison of ARIMA and exponential smoothing models in prediction of stock prices. The Journal of Prediction Markets, 17(1), 21–38.

González-Mancha, J. J., Frausto-Solís, J., Castilla Valdez, G., Terán-Villanueva, J. D., & González Barbosa, J. J. (2018). Financial time series forecasting using Simulated Annealing and Support Vector Regression. International Journal of Combinatorial Optimization Problems and Informatics, 8(2), 10–18.

Hussin, M., Ismail, Z., & Ilias, I. S. C. (2023). Bayesian Network Design for Crude Palm Oil (CPO) price prediction driven by fluctuation patterns and trends. Journal of Advanced Research in Applied Sciences and Engineering Technology, 31(2), 117–129.

Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd ed.). OTexts.

Jaquart, P., Dann, D., & Weinhardt, C. (2021). Short-term bitcoin market prediction via machine learning. The Journal of Finance and Data Science, 7, 45–66.

Kasturi, K., Salim, N., Sukprasert, A., Krishnan, R., & Hashim, U. (2017). Multivariate time series forecasting of crude palm oil price using machine learning techniques. IOP Conference Series: Materials Science and Engineering, 226, 12117.

Kasturi, K., Salim, N., Sukprasert, A., Krishnan, R., & Hashim, U. R. (2020). A comparative study on univariate time series based crude palm oil price prediction model using machine learning algorithms. International Journal of Advanced Trends in Computer Science and Engineering, 9, 5802–5806.

Li, X. M., Li Xing, D., Jin Hu, L., & Li, I. L. (2010). Building cooling load forecasting based on support vector machines with simulated annealing. Advanced Materials Research, 108–111, 1003–1008.

Lin, S. W., Lee, Z. J., Chen, S. C., & Tseng, T. Y. (2008). Parameter determination of support vector machine and feature selection using simulated annealing approach. Applied soft computing, 8(4), 1505–1512.

Madre, Y., & Devuyst, P. (2016, 29 April). Are futures the future for farmers?

Malaysia Palm Oil Council. (2020). About palm oil.

Martinez-Rios, F., & Frausto-Solis, J. (2012). A simulated annealing algorithm for the satisfiability problem using dynamic Markov chains with linear regression equilibrium. In M. d. S. G. Tsuzuki (Ed.), Simulated annealing: Advances, applications and hybridizations (pp. 1–302). InTechOpen.

Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092.

Mohd Nain, F. N., Ahamed Hassain Malim, N. H., Abdullah, R., Abdul Rahim, M. F., Ahmad Mokhtar, M. A., & Mohamad Fauzi, N. S. (2022). A review of an artificial intelligence framework for identifying the most effective palm oil prediction. Algorithms, 15(6), 218.

Obthong, M., Tantisantiwong, N., Jeamwatthanachai, W., & Wills, G. (2020). A survey on machine learning for stock price prediction: Algorithms and techniques. Paper presented at the 2nd International Conference on Finance, Economics, Management and IT Business, Vienna House Diplomat Prague, Prague, Czech Republic. 5–6 May, pp. 63–71.

Ofuoku, M., & Ngniatedema, T. (2022). Predicting the price of crude palm oil: A deep learning approach. International Journal of Strategic Decision Sciences, 13(1), 1–15.

Ojemakinde, B. (2006). Support vector regression for non-stationary time series. [Master’s thesis, University of Tennessee].

Oliva, D., Houssein, E. H., & Hinojosa, S. (2021). Metaheuristics in machine learning: Theory and applications. Berlin: Springer.

Pai, P. F., & Hong, W. C. (2005). Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Conversion and Management, 46(17), 2669–2688.

Ranjan, C. (2019, 9 May). Understanding the Kernel Trick with fundamentals. Towards Data Science.

Saadah, S., Fakhira Zahra, Z., & Hasna Haifa, Z. (2021). Support Vector Regression (SVR) dalam memprediksi harga minyak kelapa sawit di Indonesia dan nilai tukar mata uang EUR/USD [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, 85–92.

Salman, N., Lawi, A., & Syarif, S. (2018). Artificial Neural Network back propagation with particle swarm optimization for crude palm oil price prediction. Journal of Physics: Conference Series, 1114, 12088.

Shabri, A., & Hamid, M. F. A. (2019). Wavelet-support vector machine for forecasting palm oil price. Malaysian Journal of Fundamental and Applied Sciences, 15, 398-406.

Shahbandeh, M. (2021, 16 July). Export volume of palm oil worldwide in 2020/21. Statista.

Shahid, S., & Rahaman, A. (2020). Exponential smoothing methods for detection of the movement of stock prices. International Journal of Recent Technology and Engineering, 8(5), 1420–1422.

Siddique, N., & Adeli, H. (2016). Simulated annealing, its variants and engineering applications. International Journal on Artificial Intelligence Tools, 25, 1630001.

Silalahi, D. (2013). Application of Neural Network Model with genetic algorithm to predict the international price of Crude Palm Oil (CPO) and Soybean Oil (SBO). Paper presented at 12th National Convention on Statistics (NCS), EDSA Shangri-La Hotel, Mandaluyong City, Philippines, 1–2 October.

Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222.

Szykman, S., Schmidt, L. C., & Shetty, H. (1997). Improving the efficiency of simulated annealing optimization through detection of productive search. Proceedings of the ASME Design Engineering Technical Conference [Paper presentation], Sacramento, California, 14–17 September.

Tan, Y. F., Ong, L. Y., Chew, L., & Goh, Y. X. (2021). Exploring time-series forecasting models for dynamic pricing in digital signage advertising. Future Internet, 13, 241.

Xie, W., Yu, L., Xu, S., & Wang, S. (2006). A new method for crude oil price forecasting based on support vector machines. In V. N. Alexandrov, G. D. van Albada, P. M. A. Sloot, J. Dongarra (Eds.), Computational Science - ICCS 2006. ICCS 2006. Lecture Notes in Computer Science (vol. 3994). Berlin, Heidelberg: Springer.

Yee, K. W., & Samsudin, H. B. (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.

Zeng, D., Liu, Y., Jiang, L., Li, L., & Xu, G. (2012). Wick sintered temperature forecasting based on support vector machines with simulated annealing. Physics Procedia, 25, 427–434.