Prediction of rubber commodity prices using support vector regression (SVR)
DOI:
https://doi.org/10.37859/coscitech.v7i2.11975
Abstract
Natural rubber prices constitute a time-series dataset characterized by nonlinear and volatile patterns, making the prediction process challenging. This study aims to evaluate the performance of Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel and to examine the effect of hyperparameter optimization using Grid Search on natural rubber price prediction. The dataset consists of monthly global rubber prices from January 2000 to December 2020 obtained from IndexMundi, comprising a total of 252 observations. The study was conducted using two scenarios: SVR without hyperparameter optimization and SVR with hyperparameter optimization using Grid Search. Model performance was evaluated using Root Mean Squared Error (RMSE) on the testing data covering the period from January 2018 to December 2020. The results indicate that hyperparameter optimization improved model performance by reducing the RMSE from 0.188203 to 0.115199. The best hyperparameter combination obtained was C = 100, epsilon = 0.01, and gamma = 0.1, achieving a cross-validation RMSE of 0.038695. The findings demonstrate that SVR with an RBF kernel optimized through Grid Search provides better predictive performance for natural rubber prices and has the potential to support decision-making processes for farmers, industry stakeholders, and policymakers.
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References
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