Implementation of Support Vector Regression (SVR) Analysis in Predicting Gold Prices in Indonesia

DOI

10.2991/978-94-6463-332-0_12How to use a DOI?

Keywords

Gold; Kernel; Support Vector Regression (SVR)

Abstract

Gold is jewelry made from precious metals which are soft and easy to shape. The value of gold tends to fluctuate every year. Gold investments, like other investments, are not risk-free. So, investors can experience profits or even losses. Gold price predictions are needed to determine investors’ opportunities in the future. Support Vector Regression (SVR) is an application of Support Vector Machine (SVM) for regression cases whose output is real or continuous numbers. The use of the SVR method has been carried out in several studies. However, the use of the SVR method to predict the rate of gold prices in Indonesia has not been carried out. The aim of this research is to predict the rate of gold prices in Indonesia using the SVR method. The data that will be predicted is gold price data in Indonesia from October 2017 to October 2022. The SVR method is used due to the non-linear and fluctuating nature of gold price data. The study employs the Radial Basis Function (RBF) kernel considering the three parameters in the RBF kernel as gamma (𝛾), cost (C) and epsilon (𝜀)., which will be optimized using the grid search method. The results shows that the best parameters obtained are when 𝛾 = 1, C = 1, and 𝜀 = 0.1, with k = 10 because it yields the smallest error value. The level of accuracy in the prediction results is obtained using the Mean Absolute Percentage Error (MAPE) value. The MAPE value is approximately 1.28% suggesting that the prediction accuracy was very good because the MAPE value obtained was < 10%.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Suwardi Annas
AU  - Zulkifli Rais
AU  - Aswi Aswi
AU  - Indrayasaro
AU  - Nurfajriani
PY  - 2023
DA  - 2023/12/18
TI  - Implementation of Support Vector Regression (SVR) Analysis in Predicting Gold Prices in Indonesia
BT  - Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)
PB  - Atlantis Press
SP  - 97
EP  - 107
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-332-0_12
DO  - 10.2991/978-94-6463-332-0_12
ID  - Annas2023
ER  -