Support Vector Regression optimization with Particle Swam Optimization algorithm for predicting the gold prices
DOI:
https://doi.org/10.12928/bamme.v3i2.9561Keywords:
Particle Swam Optimization, Prediction, Support Vector RegressionAbstract
This paper discusses about how to predict the gold prices from 1 January 2021 to 31 January 2023. The method used in this study is the Support Vector Regression (SVR) technique, method that was developed from the support vector machine which is used as regression approach to predict future event. From the past study already know that SVR had limitation in achieving good performance because of its sensitivity to parameters. To overcome the SVR performance problems, an optimization algorithm is proposed in this study. The PSO algorithm is applied in this study to optimize the parameters of the SVR method. The results showed that the prediction of the SVR model obtained an MSE value of 0.0035744. While in the SVR model with the PSO algorithm, the MSE value is 0.0033058.
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