Bulletin of Applied Mathematics and Mathematics Education
Vol. 3 No. 2 (2023)

Support Vector Regression optimization with Particle Swam Optimization algorithm for predicting the gold prices

Selviani, Novi (Unknown)
Purwadi, Joko (Unknown)



Article Info

Publish Date
29 Dec 2023

Abstract

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.

Copyrights © 2023






Journal Info

Abbrev

BAMME

Publisher

Subject

Mathematics

Description

BAMME welcomes high-quality manuscripts resulted from a research project in the scope of applied mathematics and mathematics education, which includes, but is not limited to the following topics: Analysis and applied analysis, algebra and applied algebra, logic, geometry, differential equations, ...