Gold Price Analysis and Forecasting Using a Machine Learning Approach

DOI

10.2991/978-94-6463-978-0_33How to use a DOI?

Keywords

Gold price prediction; Machine learning; Model performance metrics; Historical data analysis; Hyperparameter

Abstract

The gold industry is highly dynamic, with gold price fluctuations affecting both buyers and sellers. Traditional valuation methods rely on expert opinions and historical data but often lack efficiency and objectivity. Recently, machine learning (ML) has gained prominence as a robust tool for analyzing complex data patterns. This research focuses on the use of ML techniques to develop an accurate gold price prediction model by analyzing extensive historical data. The proposed ML model uses feature engineering techniques such as feature selection. It also includes various algorithms, such as linear regression, random forest, SARIMAX, and gradient boosting. Model performance is evaluated through metrics such as the MAPE, RMSE, and MAE. The results indicate that the ML model significantly outperforms traditional methods in terms of accuracy, offering valuable insights for stakeholders in industries such as automotive and finance, assisting informed decisions in pricing, inventory management, and financial evaluations.

Copyright

© 2025 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  - Thilak
AU  - K. Ashwini
AU  - Manvith M. Poojary
AU  - Varun Bhat
AU  - Srinidhi Bhat
PY  - 2025
DA  - 2025/12/31
TI  - Gold Price Analysis and Forecasting Using a Machine Learning Approach
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 384
EP  - 395
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_33
DO  - 10.2991/978-94-6463-978-0_33
ID  - 2025
ER  -