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Silver Price Forecasting Using Extreme Gradient Boosting (XGBoost) Method

Author

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  • Dylan Norbert Gono

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Herlina Napitupulu

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Firdaniza

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia)

Abstract

This article presents a study on forecasting silver prices using the extreme gradient boosting (XGBoost) machine learning method with hyperparameter tuning. Silver, a valuable precious metal used in various industries and medicine, experiences significant price fluctuations. XGBoost, known for its computational efficiency and parallel processing capabilities, proves suitable for predicting silver prices. The research focuses on identifying optimal hyperparameter combinations to improve model performance. The study forecasts silver prices for the next six days, evaluating models based on mean absolute percentage error (MAPE) and root mean square error (RMSE). Model A (the best model based on MAPE value) suggests silver prices decline on the first and second days, rise on the third, decline again on the fourth, and stabilize with an increase on the fifth and sixth days. Model A achieves a MAPE of 5.98% and an RMSE of 1.6998, utilizing specific hyperparameters. Conversely, model B (the best model based on RMSE value) indicates a price decrease until the third day, followed by an upward trend until the sixth day. Model B achieves a MAPE of 6.06% and an RMSE of 1.6967, employing distinct hyperparameters. The study also compared the proposed models with several other ensemble models (CatBoost and random forest). The model comparison was carried out by incorporating 2 additional metrics (MAE and SI), and it was found that the proposed models exhibited the best performance. These findings provide valuable insights for forecasting silver prices using XGBoost.

Suggested Citation

  • Dylan Norbert Gono & Herlina Napitupulu & Firdaniza, 2023. "Silver Price Forecasting Using Extreme Gradient Boosting (XGBoost) Method," Mathematics, MDPI, vol. 11(18), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3813-:d:1233502
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    References listed on IDEAS

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    6. Al-Yahyaee, Khamis Hamed & Mensi, Walid & Sensoy, Ahmet & Kang, Sang Hoon, 2019. "Energy, precious metals, and GCC stock markets: Is there any risk spillover?," Pacific-Basin Finance Journal, Elsevier, vol. 56(C), pages 45-70.
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    Cited by:

    1. Parisa Foroutan & Salim Lahmiri, 2024. "Deep learning systems for forecasting the prices of crude oil and precious metals," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-40, December.

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