Predicting the Gold Price Based on XGBoost

Yixian Li

2024

Abstract

In recent years, accurate gold price forecasting has become increasingly important for investors, economists, and policymakers. To enhance the accuracy of such predictions, various machine learning models have been explored. This paper aims to explore the validity and practicability of using eXtreme Gradient Boosting (XGBoost) to predict gold prices. XGBoost is widely used for supervised learning problems and, as an excellent gradient boosting algorithm, it performs exceptionally well in processing structured data and time series prediction. This study constructs a prediction model based on XGBoost through historical gold price datasets, combined with market indicators and economic factors, and assesses its predictive power and stability. By evaluating the model's performance, the research seeks to determine whether XGBoost offers a reliable and efficient tool for gold price forecasting, potentially influencing financial and investment strategies. The results show that XGBoost is a robust and effective model for forecasting gold prices, providing valuable insights for informed decision-making in financial markets.

Paper Citation

in Harvard Style

Li Y. (2024). Predicting the Gold Price Based on XGBoost. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 355-359. DOI: 10.5220/0013230700004568

in Bibtex Style

@conference{ecai24,
author={Yixian Li},
title={Predicting the Gold Price Based on XGBoost},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={355-359},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013230700004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Predicting the Gold Price Based on XGBoost
SN - 978-989-758-726-9
AU - Li Y.
PY - 2024
SP - 355
EP - 359
DO - 10.5220/0013230700004568
PB - SciTePress