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Forecasting gold price with the XGBoost algorithm and SHAP interaction values

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  1. Insu Choi & Wonje Yun & Woo Chang Kim, 2025. "Improving data efficiency for analyzing global exchange rate fluctuations based on nonlinear causal network-based clustering," Annals of Operations Research, Springer, vol. 352(3), pages 745-780, September.
  2. S. E. Hill, 2022. "In-game win probability models for Canadian football," Journal of Business Analytics, Taylor & Francis Journals, vol. 5(2), pages 164-178, July.
  3. Schaumann, Sarah K. & Bergmann, Felix M. & Wagner, Stephan M. & Winkenbach, Matthias, 2023. "Route efficiency implications of time windows and vehicle capacities in first- and last-mile logistics," European Journal of Operational Research, Elsevier, vol. 311(1), pages 88-111.
  4. Bingzi Jin & Xiaojie Xu, 2025. "Predicting open interest in thermal coal futures using machine learning," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(4), pages 795-809, December.
  5. Yuan Wang & Liping Yang & Jun Wu & Zisheng Song & Li Shi, 2022. "Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method," Mathematics, MDPI, vol. 10(8), pages 1-18, April.
  6. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
  7. Ngo, Vu Minh & Nguyen, Phuc Van & Hoang, Yen Hai, 2024. "The impacts of geopolitical risks on gold, oil and financial reserve management," Resources Policy, Elsevier, vol. 90(C).
  8. Huosong Xia & Xiaoyu Hou & Justin Zuopeng Zhang & Mohammad Zoynul Abedin, 2025. "A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 112-135, January.
  9. Dong Zhang & Pengkun Wu & Chong Wu & Eric W. T. Ngai, 2024. "Forecasting duty-free shopping demand with multisource data: a deep learning approach," Annals of Operations Research, Springer, vol. 339(1), pages 861-887, August.
  10. Guangchao Li & Wei Chen & Xuepeng Zhang & Zhen Yang & Pengshuai Bi & Zhe Wang, 2022. "Ecosystem Service Values in the Dongting Lake Eco-Economic Zone and the Synergistic Impact of Its Driving Factors," IJERPH, MDPI, vol. 19(5), pages 1-17, March.
  11. Damian Ślusarczyk & Robert Ślepaczuk, 2023. "Optimal Markowitz Portfolio Using Returns Forecasted with Time Series and Machine Learning Models," Working Papers 2023-17, Faculty of Economic Sciences, University of Warsaw.
  12. Kocaarslan, Baris & Soytas, Ugur, 2023. "The role of major markets in predicting the U.S. municipal green bond market performance: New evidence from machine learning models," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
  13. Yanbo Zhang & Mengkun Liang & Haiying Ou, 2024. "Prediction of Precious Metal Index Based on Ensemble Learning and SHAP Interpretable Method," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3243-3278, December.
  14. Kocaarslan, Baris, 2024. "US dollar and oil market uncertainty: New evidence from explainable machine learning," Finance Research Letters, Elsevier, vol. 64(C).
  15. Aras, Serkan & Hanifi Van, M., 2022. "An interpretable forecasting framework for energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 328(C).
  16. Davood Pirayesh Neghab & Mucahit Cevik & M. I. M. Wahab & Ayse Basar, 2025. "Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 1857-1899, April.
  17. Saif Ali Khan & Alok Kumar Shukla & Subhash K. Yadav & Gajendra K. Vishwakarma, 2026. "Machine learning models for analysis and prediction of optimal egg production," Quality & Quantity: International Journal of Methodology, Springer, vol. 60(1), pages 1705-1725, February.
  18. Mitrentsis, Georgios & Lens, Hendrik, 2022. "An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting," Applied Energy, Elsevier, vol. 309(C).
  19. Liang, Xiaozhen & Hong, Chenxi & Chen, Jiaqi & Wang, Yingying & Yang, Mingge, 2024. "A hybrid forecasting architecture for air passenger demand considering search engine data and spatial effect," Journal of Air Transport Management, Elsevier, vol. 118(C).
  20. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
  21. Xiaojie Xu & Yun Zhang, 2023. "Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(4), pages 563-582, December.
  22. Esangbedo, Moses Olabhele & Taiwo, Blessing Olamide & Abbas, Hawraa H. & Hosseini, Shahab & Sazid, Mohammed & Fissha, Yewuhalashet, 2024. "Enhancing the exploitation of natural resources for green energy: An application of LSTM-based meta-model for aluminum prices forecasting," Resources Policy, Elsevier, vol. 92(C).
  23. 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.
  24. Hachmi Ben Ameur & Ephraim Clark & Zied Ftiti & Jean-Luc Prigent, 2024. "Operational research insights on risk, resilience & dynamics of financial & economic systems," Annals of Operations Research, Springer, vol. 334(1), pages 1-6, March.
  25. Varshini, Anu & Kayal, Parthajit & Maiti, Moinak, 2024. "How good are different machine and deep learning models in forecasting the future price of metals? Full sample versus sub-sample," Resources Policy, Elsevier, vol. 92(C).
  26. Guangchao Li & Zhaoqin Yi & Liqin Han & Ping Hu & Wei Chen & Xuefeng Ye & Zhen Yang, 2024. "The Synergistic Effect of the Same Climatic Factors on Water Use Efficiency Varies between Daily and Monthly Scales," Sustainability, MDPI, vol. 16(20), pages 1-20, October.
  27. Xiao Han & Tong Yuan & Donghui Wang & Zheng Zhao & Bing Gong, 2023. "How to understand high global food price? Using SHAP to interpret machine learning algorithm," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-20, August.
  28. Yang, Yutao & Lan, Tian, 2024. "Boosting Sports Card Sales: Leveraging Visual Display and Machine Learning in Online Retail," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
  29. Renu Sabharwal & Shah J. Miah & Samuel Fosso Wamba & Peter Cook, 2025. "Extending application of explainable artificial intelligence for managers in financial organizations," Annals of Operations Research, Springer, vol. 354(1), pages 309-339, November.
  30. Kocaarslan, Baris & Mushtaq, Rizwan, 2024. "The impact of liquidity conditions on the time-varying link between U.S. municipal green bonds and major risky markets during the COVID-19 crisis: A machine learning approach," Energy Policy, Elsevier, vol. 184(C).
  31. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
  32. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.
  33. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
  34. Mahsa Tavakoli & Rohitash Chandra & Fengrui Tian & Cristi'an Bravo, 2023. "Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams," Papers 2304.10740, arXiv.org, revised Nov 2024.
  35. Wei Li & Wolfgang Karl Hardle & Stefan Lessmann, 2022. "A Data-driven Case-based Reasoning in Bankruptcy Prediction," Papers 2211.00921, arXiv.org.
  36. Bingzi Jin & Xiaojie Xu, 2025. "Steel price index forecasts through machine learning for northwest China," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(4), pages 811-833, December.
  37. Cai Yang & Mohammad Zoynul Abedin & Hongwei Zhang & Futian Weng & Petr Hajek, 2025. "An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors," Annals of Operations Research, Springer, vol. 347(2), pages 1031-1058, April.
  38. Yang, Cai & Zhang, Hongwei & Weng, Futian, 2024. "Effects of COVID-19 vaccination programs on EU carbon price forecasts: Evidence from explainable machine learning," International Review of Financial Analysis, Elsevier, vol. 91(C).
  39. Nabavi, Zohre & Mirzehi, Mohammad & Dehghani, Hesam, 2024. "Reliable novel hybrid extreme gradient boosting for forecasting copper prices using meta-heuristic algorithms: A thirty-year analysis," Resources Policy, Elsevier, vol. 90(C).
  40. Davood Pirayesh Neghab & Mucahit Cevik & M. I. M. Wahab, 2023. "Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning," Papers 2303.16149, arXiv.org.
  41. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Mariem Nsaibi, 2023. "Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 663-687, August.
  42. Pedro Henrique Melo Albuquerque & Yaohao Peng & João Pedro Fontoura da Silva, 2022. "Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1701-1724, December.
  43. Minjun Kim & Dongbeom Kim & Geunhan Kim, 2022. "Examining the Relationship between Land Use/Land Cover (LULC) and Land Surface Temperature (LST) Using Explainable Artificial Intelligence (XAI) Models: A Case Study of Seoul, South Korea," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
  44. Yan Guo & Dezhao Tang & Wei Tang & Senqi Yang & Qichao Tang & Yang Feng & Fang Zhang, 2022. "Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
  45. Bangzhu Zhu & Chunzhuo Wan & Ping Wang & Julien Chevallier, 2025. "Forecasting carbon market volatility with big data," Annals of Operations Research, Springer, vol. 348(1), pages 317-343, May.
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