Abstract
Due to its potential for reliably anticipating gold prices, machine learning for gold price prediction has become a hot study topic in recent years, which is essential for investors and policymakers. This research did a comprehensive survey and experiments on various machine learning algorithms, such as linear regression, support vector machines, decision trees, gaussian process regression, random forest regression, polynomial features, lasso regression, k-nearest neighbor, ridge regression and neural networks, to gold price prediction. These models have been able to provide accurate predictions of gold prices, often outperforming traditional statistical methods. However, the quality of the input data and the model selection procedure affect how accurate these models are. Comprehensive experimental results showed that the support vector machine algorithm achieved the most accurate predictions, with the lowest error rates and the best R2 score.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nguyen, M.T.-T., Nguyen, V., Dinh, Jeon, J.W.: Real-time pedestrian detection using a support vector machine and stixel information. In: 2017 17th International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea (South), pp. 1350–1355 (2017). https://doi.org/10.23919/ICCAS.2017.8204203
Nguyen, V.D., Ngo, N.P., Le, Q.N., Debnath, N.C.: Robust plant leaves diseases classification using efficientnet and residual block. In: So-In, C., Londhe, N.D., Bhatt, N., Kitsing, M. (eds) Information Systems for Intelligent Systems. Smart Innovation, Systems and Technologies, vol 324. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-7447-2_12
Chau, D.H., et al.: Plant leaf diseases detection and identification using deep learning model. In: Hassanien, A.E., Rizk, R.Y., Snášel, V., Abdel-Kader, R.F. (eds.) The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 113. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-03918-8_1
Wang, J., Li, Y., Wang, T., Li, J., Wang, H., Liu, P.: A gold futures price forecast model based on SGRU-AM. IEEE Access 9, 146745–146754 (2021). https://doi.org/10.1109/ACCESS.2021.3122140
Tripurana, N., Kar, B., Chakravarty, S., Paikara, B.K., Satpathy, S.: Gold price prediction using machine learning techniques. In: ACI’22: Workshop on Advances in Computation Intelligence, Concepts & Applications at ISIC 2022, May 17–19, Savannah, United States (2022)
Sravani, M., Abhilash, Ch., Divya, T., Vasthav, Ch., Priyanka, D.: Gold price prediction. Int. J. Creative Res. Thoughts (IJCRT), 2021 IJCRT|Volume 9, Issue 6 June 2021 | ISSN: 2320-2882
Ghule, R., Gadhave, A.: Gold price prediction using machine learning. Int. J. Sci. Res. Eng. Manage. (IJSREM) 06(06), June 2022
Manjula, K.A., Karthikeyan, P.: Gold price prediction using ensemble based machine learning techniques. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, 2019, pp. 1360–1364 (2019). https://doi.org/10.1109/ICOEI.2019.8862557
Liu, C.: Price forecast for gold futures based on GA-BP neural network. In: 2009 International Conference on Management and Service Science, Beijing, China, 2009, pp. 1–4 (2009). https://doi.org/10.1109/ICMSS.2009.5302242
Patalay, S., Bandlamudi, M.R.: Gold price prediction using machine learning model trees. In: International Conference on Changing Business Paradigm (ICCBP), MDI, Murshidabad (2021)
Venkatesh, A., Saravanan, M.S.: An efficient method for predicting linear regression with polynomial regression. In: 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, pp. 1603–1606 (2022). https://doi.org/10.1109/ICOSEC54921.2022.9952049
Chen, D.: Selecting methods and its application for partners of agricultural supply chain based on Support Vector Regression. In: 2011 International Conference on Computer Science and Service System (CSSS), Nanjing, 2011, pp. 1676–1679 (2011). https://doi.org/10.1109/CSSS.2011.5974669
Jeon, H.-J., Nguyen, V.D., Duong, T.T., Jeon, J.W.: A deep learning framework for robust and real-time taillight detection under various road conditions. IEEE Trans. Intell. Transp. Syst. 23(11), 20061–20072 (2022). https://doi.org/10.1109/TITS.2022.3178697
Abdelhalim, A., Traore, I.: A new method for learning decision trees from rules. In: 2009 International Conference on Machine Learning and Applications, Miami, FL, USA, pp. 693–698 (2009). https://doi.org/10.1109/ICMLA.2009.25
Sui, Y., Zhang, L.: Visual tracking via locally structured gaussian process regression. IEEE Signal Process. Lett. 22(9), 1331–1335 (2015). https://doi.org/10.1109/LSP.2015.2402313
Dong, P., Peng, H., Cheng, X., Y., Xing, X.Z., Huang, D.: A random forest regression model for predicting residual stresses and cutting forces introduced by turning IN718 alloy. In: 2019 IEEE International Conference on Computation, Communication and Engineering (ICCCE), Fujian, China, 2019, pp. 5–8 (2019). https://doi.org/10.1109/ICCCE48422.2019.9010767
Schindler, A., Maier, G.: Object detection in gray scale images based on invariant polynomial features. In: 2010 IEEE International Conference on Image Processing, Hong Kong, China, 2010, pp. 4633–4636 (2010). https://doi.org/10.1109/ICIP.2010.5649524
Wei, H., Wang, R., Guo, Z., Yang, Z., Wang, J.: Application of Lasso regression model in the analysis of success probability of electronic experiments. In: 2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), Fuzhou, China, pp. 644–649 (2021). https://doi.org/10.1109/CEI52496.2021.9574578
Okfalisa, I.G., Mustakim, Reza, N.G.I.: Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. In: 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, pp. 294–298 (2017). https://doi.org/10.1109/ICITISEE.2017.8285514
Zhang, H., Zhang, S., Yin, Y.: An improved ELM algorithm based on EM-ELM and ridge regression. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering, IScIDE 2013. LNCS, vol. 8261. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-42057-3_95
Acknowledgments
The authors would like to thank Eastern International University (EIU) Vietnam for funding this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, D.M.T., Debnath, N.C., Quach, LD., Nguyen, V.D. (2023). Machine Learning Algorithms for Gold Price Prediction. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-43247-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43246-0
Online ISBN: 978-3-031-43247-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)