Study on Price Forecasting for Gold Commodities Using Tree-Based Customized Adaboost Algorithm
Keywords:
Gold commodity, price forecasting, tree-based customized AdaBoost (T-CA), accuracyAbstract
For buyers and sellers to make wise choices, forecasting prices in the field of commodities is essential. The goal of this study is to create a reliable and accurate price forecasting model that is tailored to gold commodities. To do this, we suggest a brand-new tree-based AdaBoost (T-CA) technique that includes features specifically designed for the features of gold prices. The methodology of the report makes use of daily statistics from the World Gold Council between 1979 and 2019. We only utilize data from 2014 for model training and the rest for model validation. The outcomes show that our suggested algorithm performs better than the alternatives, with lower errors and higher forecasting abilities. The results of this study provide a trustworthy and effective method based on the T-CA for price forecasting gold commodities. The investors, sellers, and banks can make more precise predictions and improve how they invest in the gold sector thanks to the useful insights provided by the suggested T-CA model.
Downloads
References
ul Sami, I. and Junejo, K.N., 2017. Predicting future gold rates using a machine learning approach. International Journal of Advanced Computer Science and Applications, 8(12).
Belasen, A.R. and Demirer, R., 2019. Commodity-currencies or currency-commodities: Evidence from causality tests. Resources Policy, 60, pp.162-168.
Al-Dhuraibi, W.A. and Ali, J., 2018, May. Using classification techniques to predict gold price movement. In 2018 4th International Conference on Computer and Technology Applications (ICCTA) (pp. 127-130). IEEE.
Raza, N., Ali, S., Shahzad, S.J.H. and Raza, S.A., 2018. Do commodities effectively hedge real estate risk? A multi-scale asymmetric DCC approach. Resources Policy, 57, pp.10-29.
Dolatabadi, S., Narayan, P.K., Nielsen, M.Ø. and Xu, K., 2018. The economic significance of commodity return forecasts from the fractionally cointegrated VAR model. Journal of Futures Markets, 38(2), pp.219-242.
Ayele, A.W., Gabreyohannes, E. and Tesfay, Y.Y., 2017. Macroeconomic determinants of volatility for the gold price in Ethiopia: the application of GARCH and EWMA volatility models. Global Business Review, 18(2), pp.308-326.
Benlagha, N. and El Omari, S., 2022. Connectedness of stock markets with gold and oil: New evidence from COVID-19 pandemic. Finance Research Letters, 46, p.102373.
Yurtsever, M., 2021. Gold price forecasting using LSTM, Bi-LSTM, and GRU. Avrupa Bilim ve Teknoloji Dergisi, (31), pp.341-347.
He, Z., Zhou, J., Dai, H.N. and Wang, H., 2019, August. Gold price forecast based on the LSTM-CNN model. In 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) (pp. 1046-1053). IEEE.
Weng, F., Chen, Y., Wang, Z., Hou, M., Luo, J. and Tian, Z., 2020. Gold price forecasting research based on an improved online extreme learning machine algorithm. Journal of Ambient Intelligence and Humanized Computing, 11, pp.4101-4111.
Wang, J. and Li, X., 2018. A combined neural network model for commodity price forecasting with SSA. Soft Computing, 22, pp.5323-5333.
Livieris, I.E., Pintelas, E. and Pintelas, P., 2020. A CNN–LSTM model for gold price time-series forecasting. Neural computing and applications, 32, pp.17351-17360.
Jabeur, S.B., Mefteh-Wali, S. and Viviani, J.L., 2021. Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Annals of Operations Research, pp.1-21.
Verma, S., Thampi, G.T. and Rao, M., 2020. ANN-based method for improving gold price forecasting accuracy through modified gradient descent methods. IAES International Journal of Artificial Intelligence, 9(1), p.46.
Jianwei, E., Ye, J. and Jin, H., 2019. A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting. Physica A: Statistical Mechanics and its Applications, 527, p.121454.
Dr. Antino Marelino. (2014). Customer Satisfaction Analysis based on Customer Relationship Management. International Journal of New Practices in Management and Engineering, 3(01), 07 - 12. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/26
Ahire, P. G. ., & Patil, P. D. . (2023). Context-Aware Clustering and the Optimized Whale Optimization Algorithm: An Effective Predictive Model for the Smart Grid. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 62–76. https://doi.org/10.17762/ijritcc.v11i1.5987
Downloads
Published
How to Cite
Issue
Section
License
![Creative Commons License](http://i.creativecommons.org/l/by-sa/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.