使用基于模拟退火的支持向量回归(SA-SVR)预测原油棕榈油价格

IF 0.7 Q4 BUSINESS, FINANCE
Chai Wen, Jack Goh, Amirah Chai, Rahman, Wen Eng Ong
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引用次数: 0

摘要

棕榈油是马来西亚的主要出口产品之一。预测在马来西亚衍生品交易所(BURSA Malaysia Derivative)交易的毛棕榈油期货(FCPO)的价格至关重要,因为农产品市场具有内在的不稳定性,因此比其他工业部门更容易受到价格冲击的影响。因此,如果能准确预测毛棕榈油期货合约的价格,农民、精炼厂和分销商等多方就能通过 FCPO 管理价格波动的风险。本研究提出了基于模拟退火的支持向量回归(SA-SVR)的元启发式和机器学习混合模型。该模型中的 SVR 借助模拟退火,首先确定 SVR 中要使用的最佳超参数集,然后以与实际值最小的偏差生成接近的 FCPO 价格预测值。虽然由于内存超载问题,拟议的径向基函数(RBF)核化 SA-SVR 模型只输入了 10%的训练数据,但它的预测结果令人满意,平均执行时间为 2 分 34 秒。通过使用不同的数据分割比例、不同的 SA 算法温度组合以及根据之前的最佳超参数集启动参数搜索,对模型性能进行了进一步分析。结果表明,保持测试规模不变并提取更多有关 FCPO 价格的历史数据进行模型训练,比改变训练-测试分割比例效果更好。温度安排策略表明,不同的初始和最低 SA 温度组合会影响整体优化结果。最佳组合是初始温度为 100,最低温度为 40。此外,当参数搜索空间的起点接近最佳值时,温度降低的次数和达到最佳状态的平均执行时间都会减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Crude Palm Oil Price Prediction Using Simulated Annealing-based Support Vector Regression (SA-SVR)
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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来源期刊
CiteScore
1.50
自引率
0.00%
发文量
12
审稿时长
20 weeks
期刊介绍: To provide a forum for the exchange of ideas and dissemination of empirical findings and analytical research in the specialized areas of accounting and finance with special emphasis on scholarly works with policy implications for countries in the Asia Pacific. The following are some of the topical subject areas relevant to the journal (but are not limited to): Accounting • Financial reporting and accounting standards • Auditing issues • Value based accounting and its relevance • Theory of accounting firm • Environmental auditing • Corporate governance issues • Public sector accounting Finance • Valuation of financial assets • International capital flows • Ownership and agency theory • Stock market behavior • Investment and portfolio management • Islamic banking and finance • Microstructures of financial markets
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