COMPARISON OF SUPPORT VECTOR REGRESSION AND RANDOM FOREST REGRESSION ALGORITHMS ON GOLD PRICE PREDICTIONS

Samuel Valentino Hutagalung, Yennimar Yennimar, Erikson Roni Rumapea, Michael Justin Gesitera Hia, Terkelin Sembiring, Dhanny Rukmana Manday
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Abstract

This research was conducted to test how the Support Vector Regression and Random Forest Regression algorithms predict gold futures prices. The data used in this research was taken from the Investing.com website which will later be processed into a prediction model by comparing the SVR and RVR algorithms. The Support Vector Regression and Random Forest Regression algorithms will be tested to see the performance of each prediction model. The test results show that the Support Vector Regression model is superior in terms of accuracy with a value of 83%. However, the Random Forest Regression algorithm is superior with a smaller error rate, namely with an MSE value of 270.85 and an MAE value of 12.53. Keyword: Comparison, Prediction, Support Vector Regression, Random Forest Regression.
支持向量回归与随机森林回归算法在黄金价格预测中的比较
本研究旨在检验支持向量回归和随机森林回归算法对黄金期货价格的预测效果。本研究中使用的数据来自Investing.com网站,稍后将通过比较SVR和RVR算法处理成预测模型。将测试支持向量回归和随机森林回归算法,以查看每个预测模型的性能。测试结果表明,支持向量回归模型在准确率方面具有优势,达到83%。随机森林回归算法的优势在于错误率较小,MSE为270.85,MAE为12.53。关键词:比较预测支持向量回归随机森林回归
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