{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:18:05Z","timestamp":1775913485381,"version":"3.50.1"},"reference-count":103,"publisher":"Springer Science and Business Media LLC","issue":"1-3","license":[{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T00:00:00Z","timestamp":1626998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Oper Res"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s10479-021-04187-w","type":"journal-article","created":{"date-parts":[[2021,7,23]],"date-time":"2021-07-23T11:02:56Z","timestamp":1627038176000},"page":"679-699","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":217,"title":["Forecasting gold price with the XGBoost algorithm and SHAP interaction values"],"prefix":"10.1007","volume":"334","author":[{"given":"Sami Ben","family":"Jabeur","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4082-136X","authenticated-orcid":false,"given":"Salma","family":"Mefteh-Wali","sequence":"additional","affiliation":[]},{"given":"Jean-Laurent","family":"Viviani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"key":"4187_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s11053-019-09587-1","author":"M Abd Elaziz","year":"2019","unstructured":"Abd Elaziz, M., Ewees, A. A., & Alameer, Z. (2019). Improving adaptive neuro-fuzzy inference system based on a modified salp swarm algorithm using genetic algorithms to forecast crude oil price. Natural Resources Research. https:\/\/doi.org\/10.1007\/s11053-019-09587-1","journal-title":"Natural Resources Research"},{"key":"4187_CR2","doi-asserted-by":"publisher","first-page":"3825","DOI":"10.1016\/j.eswa.2013.12.003","volume":"41","author":"J Abell\u00e1n","year":"2014","unstructured":"Abell\u00e1n, J., & Mantas, C. J. (2014). Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications, 41, 3825\u20133830. https:\/\/doi.org\/10.1016\/j.eswa.2013.12.003","journal-title":"Expert Systems with Applications"},{"issue":"April","key":"4187_CR3","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.resourpol.2019.03.003","volume":"62","author":"M Akbar","year":"2019","unstructured":"Akbar, M., Iqbal, F., & Noor, F. (2019). Bayesian analysis of dynamic linkages among gold price, stock prices, exchange rate and interest rate in Pakistan. Resources Policy, 62(April), 154\u2013164. https:\/\/doi.org\/10.1016\/j.resourpol.2019.03.003","journal-title":"Resources Policy"},{"issue":"February","key":"4187_CR4","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1016\/j.resourpol.2019.02.014","volume":"61","author":"Z Alameer","year":"2019","unstructured":"Alameer, Z., Elaziz, M. A., Ewees, A. A., Ye, H., & Jianhua, Z. (2019). Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm. Resources Policy, 61(February), 250\u2013260. https:\/\/doi.org\/10.1016\/j.resourpol.2019.02.014","journal-title":"Resources Policy"},{"key":"4187_CR5","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1016\/j.asoc.2017.06.043","volume":"60","author":"F Antunes","year":"2017","unstructured":"Antunes, F., Ribeiro, B., & Pereira, F. (2017). Probabilistic modeling and visualization for bankruptcy prediction. Applied Soft Computing, 60, 831\u2013843. https:\/\/doi.org\/10.1016\/j.asoc.2017.06.043","journal-title":"Applied Soft Computing"},{"key":"4187_CR6","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.solener.2020.01.034","volume":"198","author":"B Babar","year":"2020","unstructured":"Babar, B., Luppino, L. T., Bostr\u00f6m, T., & Anfinsen, S. N. (2020). Random forest regression for improved mapping of solar irradiance at high latitudes. Solar Energy, 198, 81\u201392. https:\/\/doi.org\/10.1016\/j.solener.2020.01.034","journal-title":"Solar Energy"},{"key":"4187_CR7","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.econmod.2019.04.021","volume":"84","author":"R Baker","year":"2020","unstructured":"Baker, R., Forrest, D., & P\u00e9rez, L. (2020). Modelling demand for lotto using a novel method of correcting for endogeneity. Economic Modelling, 84, 302\u2013308. https:\/\/doi.org\/10.1016\/j.econmod.2019.04.021","journal-title":"Economic Modelling"},{"key":"4187_CR8","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1016\/j.najef.2018.06.013","volume":"47","author":"S Basak","year":"2019","unstructured":"Basak, S., Karb, S., Sahaa, S., Luckyson, K., & Sudeepa, R. D. (2019). Predicting the direction of stock market prices using tree-based classifiers. North American Journal of Economics and Finance, 47, 552\u2013567.","journal-title":"North American Journal of Economics and Finance"},{"issue":"2","key":"4187_CR9","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.resourpol.2009.12.002","volume":"35","author":"JA Batten","year":"2010","unstructured":"Batten, J. A., Ciner, C., & Lucey, B. M. (2010). The macroeconomic determinants of volatility in precious metals markets. Resources Policy, 35(2), 65\u201371.","journal-title":"Resources Policy"},{"key":"4187_CR10","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1111\/j.1540-6288.2010.00244.x","volume":"45","author":"DG Baur","year":"2010","unstructured":"Baur, D. G., & Lucey, B. M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Finance Review, 45, 217\u2013229. https:\/\/doi.org\/10.1111\/j.1540-6288.2010.00244.x","journal-title":"Finance Review"},{"key":"4187_CR11","doi-asserted-by":"publisher","first-page":"1886","DOI":"10.1016\/j.jbankfin.2009.12.008","volume":"34","author":"DG Baur","year":"2010","unstructured":"Baur, D. G., & McDermott, T. K. (2010). Is gold a safe haven? International evidence. Journal of Banking and Finance, 34, 1886\u20131898. https:\/\/doi.org\/10.1016\/j.jbankfin.2009.12.008","journal-title":"Journal of Banking and Finance"},{"issue":"1","key":"4187_CR12","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.najef.2012.10.007","volume":"24","author":"J Beckmann","year":"2013","unstructured":"Beckmann, J., & Czudaj, R. (2013). Gold as an inflation hedge in a time-varying coefficient framework. North American Journal of Economics and Finance, 24(1), 208\u2013222. https:\/\/doi.org\/10.1016\/j.najef.2012.10.007","journal-title":"North American Journal of Economics and Finance"},{"key":"4187_CR13","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1016\/j.eneco.2019.02.002","volume":"80","author":"R Bedoui","year":"2019","unstructured":"Bedoui, R., Braiek, S., Guesmi, K., & Chevallier, J. (2019). On the conditional dependence structure between oil, gold and USD exchange rates: Nested copula based GJR GARCH model. Energy Economy, 80, 876\u2013889.","journal-title":"Energy Economy"},{"key":"4187_CR14","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.resourpol.2015.09.004","volume":"46","author":"NB Behmiri","year":"2015","unstructured":"Behmiri, N. B., & Manera, M. (2015). The role of outliers and oil price shocks on volatility of metal prices. Resources Policy, 46, 139\u2013150. https:\/\/doi.org\/10.1016\/j.resourpol.2015.09.004","journal-title":"Resources Policy"},{"issue":"8","key":"4187_CR15","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1080\/01605682.2019.1581405","volume":"71","author":"S Ben Jabeur","year":"2020","unstructured":"Ben Jabeur, S., Sadaaoui, A., Sghaier, A., & Aloui, R. (2020). Machine learning models and cost-sensitive decision trees for bond rating prediction. Journal of the Operational Research Society, 71(8), 1161\u20131179. https:\/\/doi.org\/10.1080\/01605682.2019.1581405","journal-title":"Journal of the Operational Research Society"},{"key":"4187_CR102","doi-asserted-by":"publisher","unstructured":"Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5\u201332. https:\/\/doi.org\/10.1023\/A:1010933404324.","DOI":"10.1023\/A:1010933404324"},{"key":"4187_CR16","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.resourpol.2017.12.008","volume":"55","author":"V Bhatia","year":"2018","unstructured":"Bhatia, V., Das, D., Tiwari, A. K., Shahbaz, M., & Hasim, H. M. (2018). Do precious metal spot prices influence each other? Evidence from a nonparametric causality-in-quantiles approach. Resources Policy, 55, 244\u2013252. https:\/\/doi.org\/10.1016\/j.resourpol.2017.12.008","journal-title":"Resources Policy"},{"key":"4187_CR17","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.jimonfin.2014.11.021","volume":"51","author":"V Bodart","year":"2015","unstructured":"Bodart, V., Candelon, B., & Carpantier, J.-F. (2015). Real exchanges rates, commodity prices and structural factors in developing countries. Journal of International Money and Finance, 51, 264\u2013284. https:\/\/doi.org\/10.1016\/j.jimonfin.2014.11.021","journal-title":"Journal of International Money and Finance"},{"key":"4187_CR18","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.intfin.2004.07.002","volume":"15","author":"F Capie","year":"2005","unstructured":"Capie, F., Mills, T. C., & Wood, G. (2005). Gold as a hedge against the dollar. Journal of International Financial Markets, Institutions and Money, 15, 343\u2013352.","journal-title":"Journal of International Financial Markets, Institutions and Money"},{"key":"4187_CR19","doi-asserted-by":"publisher","unstructured":"Chen, L. & Zhang, X. (2019). Gold price forecasting based on projection pursuit and neural network. IOP Conf. Series: Journal of Physics: Conf. Series 1168 06 2009. IOP Publishing Doi:https:\/\/doi.org\/10.1088\/1742-6596\/1168\/6\/062009","DOI":"10.1088\/1742-6596\/1168\/6\/062009"},{"key":"4187_CR20","doi-asserted-by":"publisher","unstructured":"Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785\u2013794). New York, NY, USA: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"4187_CR21","doi-asserted-by":"crossref","unstructured":"Chen, Y. -C., Rogoff, K. S., & Rossi, B. (2010). Can exchange rates forecast commodity prices? Quarterly Journal of Economics, 125, 1145\u20131194. http:\/\/www.jstor.org\/stable\/27867508","DOI":"10.1162\/qjec.2010.125.3.1145"},{"key":"4187_CR22","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1002\/for.2663","volume":"39","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Xie, X., Zhang, T., Bai, J., & Hou, M. (2020). A deep residual compensation extreme learning machine and applications. Journal of Forecasting, 39, 986\u2013999. https:\/\/doi.org\/10.1002\/for.2663","journal-title":"Journal of Forecasting"},{"key":"4187_CR23","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.irfa.2017.04.002","volume":"52","author":"C Ciner","year":"2017","unstructured":"Ciner, C. (2017). Predicting white metal prices by a commodity sensitive exchange rate. International Review of Financial Analysis, 52, 309\u2013315. https:\/\/doi.org\/10.1016\/j.irfa.2017.04.002","journal-title":"International Review of Financial Analysis"},{"key":"4187_CR24","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1016\/j.jbusres.2018.11.015","volume":"101","author":"F Climent","year":"2019","unstructured":"Climent, F., Momparler, A., & Carmona, P. (2019). Anticipating bank distress in the Eurozone: An Extreme Gradient Boosting approach. Journal of Business Research, 101, 885\u2013896. https:\/\/doi.org\/10.1016\/j.jbusres.2018.11.015","journal-title":"Journal of Business Research"},{"key":"4187_CR25","doi-asserted-by":"publisher","first-page":"856","DOI":"10.2139\/ssrn.843505","volume":"30","author":"A Cologni","year":"2008","unstructured":"Cologni, A., & Manera, M. (2008). Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries. Energy Economics, 30, 856\u2013888. https:\/\/doi.org\/10.2139\/ssrn.843505","journal-title":"Energy Economics"},{"key":"4187_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2019.134574","author":"D De Clercq","year":"2020","unstructured":"De Clercq, D., Wen, Z., Fei, F., Caicedo, L., Yuan, K., & Shang, R. (2020). Interpretable machine learning for predicting biomethane production in industrial-scale anaerobic co-digestion. Science of the Total Environment. https:\/\/doi.org\/10.1016\/j.scitotenv.2019.134574","journal-title":"Science of the Total Environment"},{"key":"4187_CR28","doi-asserted-by":"publisher","first-page":"101881","DOI":"10.1016\/j.resourpol.2020.101881","volume":"69","author":"P Du","year":"2020","unstructured":"Du, P., Wang, J., Yang, W., & Tong, N. (2020). Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine. Resources Policy, 69, 101881.","journal-title":"Resources Policy"},{"key":"4187_CR29","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1016\/j.energy.2019.04.155","volume":"178","author":"B Elie","year":"2019","unstructured":"Elie, B., Naji, J., Dutta, A., & Uddin, G. S. (2019). Gold and crude oil as safe-haven assets for clean energy stock indices: Blended copulas approach. Energy, 178, 544\u2013553. https:\/\/doi.org\/10.1016\/j.energy.2019.04.155","journal-title":"Energy"},{"key":"4187_CR30","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1002\/(SICI)1099-131X(199803)17:2<81::AID-FOR680>3.0.CO;2-B","volume":"17","author":"A Escribano","year":"1998","unstructured":"Escribano, A., & Granger, C. W. J. (1998). Investigating the relationship between gold and silver prices. Journal of Forecasting, 17, 81\u2013107.","journal-title":"Journal of Forecasting"},{"key":"4187_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.resourpol.2019.101555","author":"AA Ewees","year":"2020","unstructured":"Ewees, A. A., Elaziz, M. A., Alameer, Z., Ye, H., & Jianhua, Z. (2020). Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility. Resources Policy. https:\/\/doi.org\/10.1016\/j.resourpol.2019.101555","journal-title":"Resources Policy"},{"key":"4187_CR33","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/S0164-0704(87)80007-1","volume":"9","author":"JN Fortune","year":"1987","unstructured":"Fortune, J. N. (1987). The inflation rate of the price of gold, expected prices and interest rates. Journal of Macroeconomy, 9, 71\u201382.","journal-title":"Journal of Macroeconomy"},{"key":"4187_CR34","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29, 1189\u20131232.","journal-title":"The Annals of Statistics"},{"key":"4187_CR35","unstructured":"Gholamy, A., Kreinovich, V., Kosheleva, O., (2018). Why 70\/30 or 80\/20 relation between training and testing sets\u202f: A pedagogical explanation. Departmental Technical Reports (CS) 1\u20136."},{"key":"4187_CR36","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.comnet.2019.01.026","volume":"151","author":"J Guo","year":"2019","unstructured":"Guo, J., Yang, L., Bie, R., Yu, J., Gao, Y., Shen, Y., & Kos, A. (2019). An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring. Computer Networks, 151, 166\u2013180. https:\/\/doi.org\/10.1016\/j.comnet.2019.01.026","journal-title":"Computer Networks"},{"key":"4187_CR37","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1016\/j.eneco.2009.12.005","volume":"32","author":"Y He","year":"2010","unstructured":"He, Y., Wang, S., & Lai, K. K. (2010). Global economic activity and crude oil prices: A cointegration analysis. Energy Economics, 32, 868\u2013876. https:\/\/doi.org\/10.1016\/j.eneco.2009.12.005","journal-title":"Energy Economics"},{"key":"4187_CR103","unstructured":"Herawati, S., Firmansyah, A., Latif, M., & Aeri, R. (2017). Implementing method of ensemble empirical mode decomposition and recurrent neural network for gold price forecasting. Journal of Engineering Research and Applications, 7(11), 39\u201343."},{"key":"4187_CR38","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.ejor.2019.04.013","volume":"278","author":"N Huck","year":"2019","unstructured":"Huck, N. (2019). Large data sets and machine learning: Applications to statistical arbitrage. European Journal of Operational Research, 278, 330\u2013342.","journal-title":"European Journal of Operational Research"},{"issue":"1","key":"4187_CR39","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.ejor.2019.01.072","volume":"277","author":"C Jiang","year":"2019","unstructured":"Jiang, C., Wang, Z., & Zhao, H. (2019). A prediction-driven mixture cure model and its application in credit scoring. European Journal of Operational Research, 277(1), 20\u201331. https:\/\/doi.org\/10.1016\/j.ejor.2019.01.072","journal-title":"European Journal of Operational Research"},{"key":"4187_CR40","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.eneco.2016.12.011","volume":"62","author":"SH Kang","year":"2017","unstructured":"Kang, S. H., McIver, R., & Yoon, S.-M. (2017). Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets. Energy Economics, 62, 19\u201332.","journal-title":"Energy Economics"},{"issue":"March","key":"4187_CR41","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.resourpol.2017.04.001","volume":"52","author":"K Kanjilal","year":"2017","unstructured":"Kanjilal, K., & Ghosh, S. (2017). Dynamics of crude oil and gold price post 2008 global financial crisis\u2014New evidence from threshold vector error-correction model. Resources Policy, 52(March), 358\u2013365. https:\/\/doi.org\/10.1016\/j.resourpol.2017.04.001","journal-title":"Resources Policy"},{"key":"4187_CR42","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 2017-Decem(Nips), 3147\u20133155."},{"key":"4187_CR43","doi-asserted-by":"publisher","first-page":"884","DOI":"10.1016\/j.qref.2008.08.005","volume":"49","author":"A Kearney","year":"2009","unstructured":"Kearney, A., & Lombra, R. E. (2009). Gold and platinum: Toward solving the price puzzle. The Quarterly Review of Economics and Finance, 49, 884\u2013892.","journal-title":"The Quarterly Review of Economics and Finance"},{"key":"4187_CR44","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.eswa.2009.05.044","volume":"37","author":"M Khashei","year":"2010","unstructured":"Khashei, M., & Bijari, M. (2010). An artificial neural network model for time series forecasting. Expert System and Applications, 37, 479\u2013489. https:\/\/doi.org\/10.1016\/j.eswa.2009.05.044","journal-title":"Expert System and Applications"},{"key":"4187_CR45","doi-asserted-by":"publisher","first-page":"2664","DOI":"10.1016\/j.asoc.2010.10.015","volume":"11","author":"M Khashei","year":"2011","unstructured":"Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Application Soft Computation, 11, 2664\u20132675. https:\/\/doi.org\/10.1016\/j.asoc.2010.10.015","journal-title":"Application Soft Computation"},{"issue":"2","key":"4187_CR46","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.ejor.2016.10.031","volume":"259","author":"C Krauss","year":"2017","unstructured":"Krauss, C., Anh, X., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 R. European Journal of Operational Research, 259(2), 689\u2013702. https:\/\/doi.org\/10.1016\/j.ejor.2016.10.031","journal-title":"European Journal of Operational Research"},{"key":"4187_CR47","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.eswa.2017.05.024","volume":"84","author":"W Kristjanpoller","year":"2017","unstructured":"Kristjanpoller, W., & Hernandez, E. (2017). Volatility of main metals forecasted by a hybrid ANN-GARCH model with regressors. Expert Systems with Applications, 84, 290\u2013300. https:\/\/doi.org\/10.1016\/j.eswa.2017.05.024","journal-title":"Expert Systems with Applications"},{"issue":"20","key":"4187_CR48","doi-asserted-by":"publisher","first-page":"7245","DOI":"10.1016\/j.eswa.2015.04.058","volume":"42","author":"W Kristjanpoller","year":"2015","unstructured":"Kristjanpoller, W., & Minutolo, M. C. (2015). Gold price volatility: A forecasting approach using the artificial neural network-GARCH model. Expert Systems with Applications, 42(20), 7245\u20137251. https:\/\/doi.org\/10.1016\/j.eswa.2015.04.058","journal-title":"Expert Systems with Applications"},{"key":"4187_CR49","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.eswa.2016.08.045","volume":"65","author":"W Kristjanpoller","year":"2016","unstructured":"Kristjanpoller, W., & Minutolo, M. C. (2016). Forecasting volatility of oil price using an artificial neural network-GARCH model. Expert Systems with Applications, 65, 233\u2013241. https:\/\/doi.org\/10.1016\/j.eswa.2016.08.045","journal-title":"Expert Systems with Applications"},{"key":"4187_CR50","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.qref.2016.09.005","volume":"65","author":"O Kucher","year":"2017","unstructured":"Kucher, O., & McCoskey, S. (2017). The long-run relationship between precious metal prices and the business cycle. Quarterly Review of Economics and Finance, 65, 263\u2013275. https:\/\/doi.org\/10.1016\/j.qref.2016.09.005","journal-title":"Quarterly Review of Economics and Finance"},{"issue":"3","key":"4187_CR51","doi-asserted-by":"publisher","first-page":"63","DOI":"10.5750\/jpm.v12i3.1669","volume":"12","author":"S Kumar","year":"2018","unstructured":"Kumar, S. (2018). Prediction of gold and silver prices in an emerging economy: Comparative analysis of linear, nonlinear, hybrid, and ensemble models. The Journal of Prediction Markets, 12(3), 63\u201378.","journal-title":"The Journal of Prediction Markets"},{"issue":"April","key":"4187_CR52","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.apenergy.2018.02.069","volume":"221","author":"J Lago","year":"2018","unstructured":"Lago, J., De Ridder, F., & De Schutter, B. (2018). Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms. Applied Energy, 221(April), 386\u2013405. https:\/\/doi.org\/10.1016\/j.apenergy.2018.02.069","journal-title":"Applied Energy"},{"key":"4187_CR53","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1016\/j.eneco.2006.10.010","volume":"30","author":"S Lardic","year":"2008","unstructured":"Lardic, S., & Mignon, V. (2008). Oil prices and economic activity: An asymmetric cointegration approach. Energy Economics, 30, 847\u2013855. https:\/\/doi.org\/10.1016\/j.eneco.2006.10.010","journal-title":"Energy Economics"},{"key":"4187_CR54","first-page":"1673","volume":"5","author":"M Lineesh","year":"2010","unstructured":"Lineesh, M., Minu, K., & John, C. J. (2010). Analysis of nonstationary nonlinear economic time series of gold price: A comparative study. International Mathematical Forum, 5, 1673\u20131683.","journal-title":"International Mathematical Forum"},{"key":"4187_CR55","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.resourpol.2017.05.007","volume":"52","author":"C Liu","year":"2017","unstructured":"Liu, C., Hu, Z., Li, Y., & Liu, S. (2017). Forecasting copper prices by decision tree learning. Resources Policy, 52, 427\u2013434. https:\/\/doi.org\/10.1016\/j.resourpol.2017.05.007","journal-title":"Resources Policy"},{"key":"4187_CR56","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.resourpol.2018.12.009","volume":"60","author":"Y Liu","year":"2019","unstructured":"Liu, Y., Li, H., Guan, J., Liu, X., Guan, Q., & Sun, Q. (2019). Influence of different factors on prices of upstream, middle and downstream products in China\u2019s whole steel industry chain: Based on Adaptive Neural Fuzzy Inference. System Resources Policy, 60, 134\u2013142. https:\/\/doi.org\/10.1016\/j.resourpol.2018.12.009","journal-title":"System Resources Policy"},{"issue":"January","key":"4187_CR57","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.dss.2018.08.010","volume":"114","author":"ALD Loureiro","year":"2018","unstructured":"Loureiro, A. L. D., Migu\u00e9is, V. L., & da Silva, L. F. M. (2018). Exploring the use of deep neural networks for sales forecasting in fashion retail. Decision Support Systems, 114(January), 81\u201393. https:\/\/doi.org\/10.1016\/j.dss.2018.08.010","journal-title":"Decision Support Systems"},{"key":"4187_CR58","unstructured":"Lundberg, S. M., Erion, G. G., & Lee, S. -I. (2018). Consistent Individualized Feature Attribution for Tree Ensembles. 2."},{"key":"4187_CR59","unstructured":"Lundberg, S. M., and Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2017-Decem(Section 2), 4766\u20134775."},{"key":"4187_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.118955","author":"J Ma","year":"2020","unstructured":"Ma, J., Ding, Y., Cheng, J. C. P., Jiang, F., Tan, Y., Gan, V. J. L., & Wan, Z. (2020). Identification of high impact factors of air quality on a national scale using big data and machine learning techniques. Journal of Cleaner Production. https:\/\/doi.org\/10.1016\/j.jclepro.2019.118955","journal-title":"Journal of Cleaner Production"},{"issue":"5","key":"4187_CR61","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/S0148-6195(97)00034-9","volume":"49","author":"S Mahdavi","year":"1997","unstructured":"Mahdavi, S., & Zhou, S. (1997). Gold and commodity prices as leading indicators of inflation: Tests of long-run relationship and predictive performance. Journal of Economics and Business, 49(5), 475\u2013489.","journal-title":"Journal of Economics and Business"},{"issue":"1","key":"4187_CR62","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.ejor.2019.02.005","volume":"277","author":"M Mercadier","year":"2019","unstructured":"Mercadier, M., & Lardy, J. P. (2019). Credit spread approximation and improvement using random forest regression. European Journal of Operational Research, 277(1), 351\u2013365. https:\/\/doi.org\/10.1016\/j.ejor.2019.02.005","journal-title":"European Journal of Operational Research"},{"key":"4187_CR63","doi-asserted-by":"publisher","first-page":"984","DOI":"10.1016\/j.physa.2017.09.091","volume":"491","author":"B Mo","year":"2018","unstructured":"Mo, B., Nie, H., & Jiang, Y. (2018). Dynamic linkages among the gold market, US dollar and crude oil market. Physics A, 491, 984\u2013994. https:\/\/doi.org\/10.1016\/j.physa.2017.09.091","journal-title":"Physics A"},{"key":"4187_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2019.109564","author":"H Mo","year":"2019","unstructured":"Mo, H., Sun, H., Liu, J., & Wei, S. (2019). Developing window behavior models for residential buildings using XGBoost algorithm. Energy and Buildings. https:\/\/doi.org\/10.1016\/j.enbuild.2019.109564","journal-title":"Energy and Buildings"},{"key":"4187_CR65","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.irfa.2015.07.005","volume":"41","author":"FA O\u2019connor","year":"2015","unstructured":"O\u2019connor, F. A., Lucey, B. M., Battend, J. A., & Baure, D. G. (2015). The financial economics of gold\u2014A survey. International Review of Financial Analysis, 41, 186\u2013205.","journal-title":"International Review of Financial Analysis"},{"key":"4187_CR66","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1016\/j.mulfin.2007.12.002","volume":"18","author":"A Parisi","year":"2008","unstructured":"Parisi, A., Parisi, F., & D\u00edaz, D. (2008). Forecasting gold price changes: Rolling and recursive neural network models. Journal of Multinational Financial Management, 18, 477\u2013487. https:\/\/doi.org\/10.1016\/j.mulfin.2007.12.002","journal-title":"Journal of Multinational Financial Management"},{"key":"4187_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ecosta.2018.10.001","volume":"11","author":"MH Pesaran","year":"2019","unstructured":"Pesaran, M. H., & Smith, R. P. (2019). A Bayesian analysis of linear regression models with highly collinear regressors. Econometrics and Statistics, 11, 1\u201321. https:\/\/doi.org\/10.1016\/j.ecosta.2018.10.001","journal-title":"Econometrics and Statistics"},{"key":"4187_CR68","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1080\/13504851.2014.925040","volume":"22","author":"C Pierdzioch","year":"2015","unstructured":"Pierdzioch, C., Risse, M., & Rohloff, S. (2015a). Forecasting gold-price fluctuations: A real-time boosting approach. Applied Economics Letter, 22, 46\u201350.","journal-title":"Applied Economics Letter"},{"key":"4187_CR69","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1080\/13504851.2014.925040","volume":"22","author":"C Pierdzioch","year":"2015","unstructured":"Pierdzioch, C., Risse, M., & Rohloff, S. (2015b). A boosting approach to forecasting gold and silver returns: Economic and statistical forecast evaluation. Applied Economics Letter, 22, 46\u201350.","journal-title":"Applied Economics Letter"},{"key":"4187_CR104","doi-asserted-by":"publisher","unstructured":"Pierdzioch, C., Risse, M., & Rohloff, S. (2016). Fluctuations of the real exchange rate, real interest rates, and the dynamics of the price of gold in a small open economy. Empirical Economics, 51(4), 1481\u20131499. https:\/\/doi.org\/10.1007\/s00181-015-1053-5","DOI":"10.1007\/s00181-015-1053-5"},{"key":"4187_CR71","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1007\/s00181-018-1558-9","volume":"58","author":"P Pierdziochu","year":"2020","unstructured":"Pierdziochu, P., & Risse, M. (2020). Forecasting precious metal returns with multivariate random forests. Empirical Economics, 58, 1167\u20131184. https:\/\/doi.org\/10.1007\/s00181-018-1558-9","journal-title":"Empirical Economics"},{"key":"4187_CR72","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.irfa.2018.02.005","volume":"57","author":"J Pi\u00f1eiro-Chousa","year":"2018","unstructured":"Pi\u00f1eiro-Chousa, J., L\u00f3pez-Cabarcos, M. \u00c1., P\u00e9rez-Pico, A. M., & Ribeiro-Navarrete, B. (2018). Does social network sentiment influence the relationship between the S&P 500 and gold returns? International Review of Financial Analysis, 57, 57\u201364. https:\/\/doi.org\/10.1016\/j.irfa.2018.02.005","journal-title":"International Review of Financial Analysis"},{"key":"4187_CR73","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). Catboost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 2018-December (Section 4), 6638\u20136648."},{"key":"4187_CR74","doi-asserted-by":"publisher","first-page":"2070","DOI":"10.1016\/j.jbankfin.2011.01.014","volume":"35","author":"K Pukthuanthong","year":"2011","unstructured":"Pukthuanthong, K., & Roll, R. (2011). Gold and the dollar (and the Euro, Pound, and Yen). Journal of Banking and Finance, 35, 2070\u20132083.","journal-title":"Journal of Banking and Finance"},{"issue":"3","key":"4187_CR75","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1080\/0952813X.2019.1652356","volume":"32","author":"B Rabbouch","year":"2020","unstructured":"Rabbouch, B., Sa\u00e2daoui, F., & Mraihi, R. (2020). Empirical-type simulated annealing for solving the capacitated vehicle routing problem. Journal of Experimental and Theoretical Artificial Intelligence, 32(3), 437\u2013452. https:\/\/doi.org\/10.1080\/0952813X.2019.1652356","journal-title":"Journal of Experimental and Theoretical Artificial Intelligence"},{"key":"4187_CR76","doi-asserted-by":"publisher","DOI":"10.1007\/s10614-017-9764-7","author":"S Ramyar","year":"2017","unstructured":"Ramyar, S., & Kianfar, F. (2017). Forecasting crude oil prices: A comparison between artificial neural networks and vector Autoregressive models. Computational Economics. https:\/\/doi.org\/10.1007\/s10614-017-9764-7","journal-title":"Computational Economics"},{"key":"4187_CR77","doi-asserted-by":"publisher","first-page":"2665","DOI":"10.1016\/j.jbankfin.2013.03.020","volume":"37","author":"JC Reboredo","year":"2013","unstructured":"Reboredo, J. C. (2013). Is gold a safe haven or a hedge for the U.S. dollar? Implications for risk management. Journal of Banking and Finance, 37, 2665\u20132676.","journal-title":"Journal of Banking and Finance"},{"issue":"2","key":"4187_CR78","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1016\/j.ijforecast.2018.11.008","volume":"35","author":"M Risse","year":"2019","unstructured":"Risse, M. (2019). Combining wavelet decomposition with machine learning to forecast gold returns. International Journal of Forecasting, 35(2), 601\u2013615. https:\/\/doi.org\/10.1016\/j.ijforecast.2018.11.008","journal-title":"International Journal of Forecasting"},{"key":"4187_CR105","unstructured":"Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Modelagnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386."},{"key":"4187_CR79","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.resourpol.2009.02.001","volume":"34","author":"MC Roberts","year":"2009","unstructured":"Roberts, M. C. (2009). Duration and characteristics of metal price cycles. Resources Policy, 34, 87\u2013102. https:\/\/doi.org\/10.1016\/j.resourpol.2009.02.001","journal-title":"Resources Policy"},{"key":"4187_CR80","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.resourpol.2015.06.002","volume":"45","author":"A Rossen","year":"2015","unstructured":"Rossen, A. (2015). What are metal prices like? Co-movement, price cycles and long-run trends. Resources Policy, 45, 255\u2013276. https:\/\/doi.org\/10.1016\/j.resourpol.2015.06.002","journal-title":"Resources Policy"},{"issue":"1","key":"4187_CR81","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1080\/14697681003591712","volume":"12","author":"F Sa\u00e2daoui","year":"2012","unstructured":"Sa\u00e2daoui, F. (2012). A probabilistic clustering method for US interest rate analysis. Quantitative Finance, 12(1), 135\u2013148. https:\/\/doi.org\/10.1080\/14697681003591712","journal-title":"Quantitative Finance"},{"key":"4187_CR82","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.eneco.2009.08.010","volume":"32","author":"R Sari","year":"2010","unstructured":"Sari, R., Hammoudeh, S., & Soytas, U. (2010). Dynamics of oil price, precious metal prices, and exchange rate. Energy Economics, 32, 351\u2013362.","journal-title":"Energy Economics"},{"key":"4187_CR83","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.jbankfin.2017.11.010","volume":"88","author":"K Schweikert","year":"2018","unstructured":"Schweikert, K. (2018). Are gold and silver cointegrated? New evidence from quantile cointegrating regressions. Journal of Banking and Finance, 88, 44\u201351. https:\/\/doi.org\/10.1016\/j.jbankfin.2017.11.010","journal-title":"Journal of Banking and Finance"},{"key":"4187_CR84","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/j.resourpol.2013.08.004","volume":"38","author":"A Sensoy","year":"2013","unstructured":"Sensoy, A. (2013). Dynamic relationship between precious metals. Resources Policy, 38, 504\u2013511.","journal-title":"Resources Policy"},{"key":"4187_CR85","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/j.eneco.2018.02.022","volume":"71","author":"P Sephton","year":"2018","unstructured":"Sephton, P., & Mann, J. (2018). Gold and crude oil prices after the great moderation. Energy Economics, 71, 273\u2013281. https:\/\/doi.org\/10.1016\/j.eneco.2018.02.022","journal-title":"Energy Economics"},{"key":"4187_CR86","doi-asserted-by":"publisher","first-page":"106181","DOI":"10.1016\/j.asoc.2020.106181","volume":"90","author":"OB Sezer","year":"2020","unstructured":"Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005\u20132019. Applied Soft Computing, 90, 106181.","journal-title":"Applied Soft Computing"},{"key":"4187_CR87","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.resourpol.2010.05.004","volume":"35","author":"S Shafiee","year":"2010","unstructured":"Shafiee, S., & Topal, E. (2010a). An overview of global gold market and gold price forecasting. Resources Policy, 35, 178\u2013189. [4].","journal-title":"Resources Policy"},{"key":"4187_CR88","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.resourpol.2010.05.004","volume":"35","author":"S Shafiee","year":"2010","unstructured":"Shafiee, S., & Topal, E. (2010b). An overview of global gold market and gold price forecasting. Resources Policy, 35, 178\u2013189. https:\/\/doi.org\/10.1016\/j.resourpol.2010.05.004","journal-title":"Resources Policy"},{"key":"4187_CR89","doi-asserted-by":"crossref","unstructured":"Shapley, L. S., (1953). A value for n-person games. Contrib. to Theory Games. pp. 307\u2013317.","DOI":"10.1515\/9781400881970-018"},{"key":"4187_CR90","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.eswa.2019.04.029","volume":"130","author":"N Singh","year":"2019","unstructured":"Singh, N., Singh, P., & Bhagat, D. (2019). A rule extraction approach from support vector machines for diagnosing hypertension among diabetics. Expert Systems with Applications, 130, 188\u2013205. https:\/\/doi.org\/10.1016\/j.eswa.2019.04.029","journal-title":"Expert Systems with Applications"},{"issue":"January","key":"4187_CR91","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.resourpol.2019.01.004","volume":"60","author":"S Singhal","year":"2019","unstructured":"Singhal, S., Choudhary, S., & Biswal, P. C. (2019). Return and volatility linkages among International crude oil price, gold price, exchange rate and stock markets: Evidence from Mexico. Resources Policy, 60(January), 255\u2013261. https:\/\/doi.org\/10.1016\/j.resourpol.2019.01.004","journal-title":"Resources Policy"},{"key":"4187_CR106","doi-asserted-by":"crossref","unstructured":"\u0160trumbelj, E., & Kononenko, I. (2014). Explaining prediction models and individual predictions with feature contributions. Knowledge and information systems, 41(3), 647\u2013665.","DOI":"10.1007\/s10115-013-0679-x"},{"key":"4187_CR92","doi-asserted-by":"publisher","DOI":"10.1016\/j.frl.2018.12.032","author":"X Sun","year":"2019","unstructured":"Sun, X., Liu, M., & Sima, Z. (2019). A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, December. https:\/\/doi.org\/10.1016\/j.frl.2018.12.032","journal-title":"Finance Research Letters, December."},{"key":"4187_CR93","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1007\/978-3-319-70942-0_47","volume-title":"Predictive Econometrics and Big Data","author":"T Teetranont","year":"2018","unstructured":"Teetranont, T., Chanaim, S., Yamaka, W., & Sriboonchitta, S. (2018). Investigating relationship between gold price and crude oil price using interval data with copula based GARCH. In V. Kreinovich, S. Sriboonchitta, & N. Chakpitak (Eds.), Predictive Econometrics and Big Data (pp. 656\u2013669). Springer International Publishing."},{"issue":"2","key":"4187_CR94","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.ribaf.2006.07.001","volume":"21","author":"E Tully","year":"2007","unstructured":"Tully, E., & Lucey, B. M. (2007). A power GARCH examination of the gold market. Research in International Business and Finance, 21(2), 316\u2013325.","journal-title":"Research in International Business and Finance"},{"key":"4187_CR95","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1142\/S0219622016500504","volume":"16","author":"X Wen","year":"2017","unstructured":"Wen, X., Yang, X., & Gong, K. K. L. (2017). Multi-scale volatility feature analysis and prediction of gold price. International Journal of Information Technology and Decision, 16, 205\u2013223.","journal-title":"International Journal of Information Technology and Decision"},{"key":"4187_CR96","doi-asserted-by":"publisher","first-page":"2784","DOI":"10.1016\/S1003-6326(16)64395-9","volume":"26","author":"D Wu","year":"2016","unstructured":"Wu, D., & Hu, Z.-H. (2016). Structural changes and volatility correlation in nonferrous metal market. Transactions Nonferrous Metals Society of China, 26, 2784\u20132792. https:\/\/doi.org\/10.1016\/S1003-6326(16)64395-9","journal-title":"Transactions Nonferrous Metals Society of China"},{"key":"4187_CR97","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.eswa.2017.02.017","volume":"78","author":"Y Xia","year":"2017","unstructured":"Xia, Y., Liu, C., Li, Y. Y., & Liu, N. (2017). A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Systems with Applications, 78, 225\u2013241. https:\/\/doi.org\/10.1016\/j.eswa.2017.02.017","journal-title":"Expert Systems with Applications"},{"key":"4187_CR99","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1016\/S1003-6326(15)63693-7","volume":"25","author":"Y Yue","year":"2015","unstructured":"Yue, Y., Liu, D., & Xu, S. (2015). Price linkage between Chinese and international nonferrous metals commodity markets based on VAR-DCC-GARCH models. Transactions Nonferrous Metals Society of China, 25, 1020\u20131026. https:\/\/doi.org\/10.1016\/S1003-6326(15)63693-7","journal-title":"Transactions Nonferrous Metals Society of China"},{"key":"4187_CR100","doi-asserted-by":"publisher","first-page":"101806","DOI":"10.1016\/j.resourpol.2020.101806","volume":"69","author":"P Zhang","year":"2020","unstructured":"Zhang, P., & Ci, B. (2020). Deep belief network for gold price forecasting. Resources Policy, 69, 101806.","journal-title":"Resources Policy"},{"key":"4187_CR101","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s00521-017-3039-z","volume":"31","author":"J Zheng","year":"2019","unstructured":"Zheng, J., Fu, X., & Zhang, G. (2019). Research on exchange rate forecasting based on deep belief network. Neural Computing and Applications, 31, 573\u2013582.","journal-title":"Neural Computing and Applications"},{"key":"4187_CR107","doi-asserted-by":"crossref","unstructured":"Zhu, Y., & Zhang, C. (2018). Gold price prediction based on pca-ga-bp neural network. Journal of Computer and Communications, 6(7), 22\u201333.","DOI":"10.4236\/jcc.2018.67003"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-021-04187-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-021-04187-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-021-04187-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,17]],"date-time":"2024-03-17T18:06:35Z","timestamp":1710698795000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-021-04187-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,23]]},"references-count":103,"journal-issue":{"issue":"1-3","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["4187"],"URL":"https:\/\/doi.org\/10.1007\/s10479-021-04187-w","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,23]]},"assertion":[{"value":"24 June 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}