{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T23:22:00Z","timestamp":1781565720650,"version":"3.54.5"},"reference-count":94,"publisher":"Springer Science and Business Media LLC","issue":"2-3","license":[{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"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":[[2025,2]]},"DOI":"10.1007\/s10479-023-05400-8","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T14:02:38Z","timestamp":1689170558000},"page":"979-1002","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Forecasting oil price in times of crisis: a new evidence from machine learning versus deep learning models\u00a0"],"prefix":"10.1007","volume":"345","author":[{"given":"Haithem","family":"Awijen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hachmi","family":"Ben Ameur","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6216-1104","authenticated-orcid":false,"given":"Zied","family":"Ftiti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wa\u00ebl","family":"Louhichi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"5400_CR1","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.ejpe.2019.06.006","volume":"28","author":"AK Abbas","year":"2019","unstructured":"Abbas, A. K., Al-haideri, N. A., & Bashikh, A. A. (2019). Implementing artificial neural networks and support vector machines to predict lost circulation. Egyptian Journal of Petroleum, 28, 339\u2013347.","journal-title":"Egyptian Journal of Petroleum"},{"key":"5400_CR2","doi-asserted-by":"crossref","unstructured":"Akhtaruzzaman, M., Boubaker, S., Chiah, M. & Zhong, A. (2020). Covid- 19 and oil price risk exposure. Finance Research Letters, 101882.","DOI":"10.1016\/j.frl.2020.101882"},{"key":"5400_CR3","doi-asserted-by":"publisher","first-page":"101882","DOI":"10.1016\/j.frl.2020.101882","volume":"42","author":"M Akhtaruzzaman","year":"2021","unstructured":"Akhtaruzzaman, M., Boubaker, S., Chiah, M., & Zhong, A. (2021). Covid- 19 and oil price risk exposure. Finance Research Letters, 42, 101882.","journal-title":"Finance Research Letters"},{"key":"5400_CR4","doi-asserted-by":"publisher","first-page":"100013","DOI":"10.1016\/j.mlwa.2020.100013","volume":"3","author":"R Al-Shabandar","year":"2021","unstructured":"Al-Shabandar, R., Jaddoa, A., Liatsis, P., & Hussain, A. J. (2021). A deep gated recurrent neural network for petroleum production forecasting. Machine Learning with Applications, 3, 100013.","journal-title":"Machine Learning with Applications"},{"key":"5400_CR5","unstructured":"Ameur, H. B., Ftiti, Z., Jawadi, F., & Louhichi, W. (2020). Measuring extreme risk dependence between the oil and gas markets. Annals of Operations Research, 1\u201318."},{"key":"5400_CR6","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/0169-2070(92)90008-W","volume":"8","author":"JS Armstrong","year":"1992","unstructured":"Armstrong, J. S., & Collopy, F. (1992). Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 8, 69\u201380.","journal-title":"International Journal of Forecasting"},{"key":"5400_CR7","doi-asserted-by":"publisher","first-page":"110861","DOI":"10.1016\/j.chaos.2021.110861","volume":"146","author":"K ArunKumar","year":"2021","unstructured":"ArunKumar, K., Kalaga, D. V., Kumar, C. M. S., Kawaji, M., & Brenza, T. M. (2021). Forecasting of covid-19 using deep layer recurrent neural networks (rnns) with gated recurrent units (grus) and long short-term memory (lstm) cells. Chaos, Solitons & Fractals, 146, 110861.","journal-title":"Chaos, Solitons & Fractals"},{"key":"5400_CR8","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5, 157\u2013166.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"5400_CR9","unstructured":"Brownlee, J. (2018). Deep learning for time series forecasting: predict the future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery."},{"key":"5400_CR10","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.cor.2018.01.013","volume":"106","author":"A Candelieri","year":"2019","unstructured":"Candelieri, A., Giordani, I., Archetti, F., Barkalov, K., Meyerov, I., Polovinkin, A., Sysoyev, A., & Zolotykh, N. (2019). Tuning hyperparameters of a svm-based water demand forecasting system through parallel global optimization. Computers & Operations Research, 106, 202\u2013209.","journal-title":"Computers & Operations Research"},{"key":"5400_CR11","doi-asserted-by":"publisher","first-page":"129246","DOI":"10.1016\/j.jclepro.2021.129246","volume":"326","author":"Z Cao","year":"2021","unstructured":"Cao, Z., Han, X., Lyons, W., & O\u2019Rourke, F. (2021). Energy management optimisation using a combined long short-term memory recurrent neural network-particle swarm optimisation model. Journal of Cleaner Production, 326, 129246.","journal-title":"Journal of Cleaner Production"},{"key":"5400_CR12","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.energy.2018.12.016","volume":"169","author":"Z Cen","year":"2019","unstructured":"Cen, Z., & Wang, J. (2019). Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer. Energy, 169, 160\u2013171.","journal-title":"Energy"},{"key":"5400_CR13","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.neucom.2019.10.118","volume":"408","author":"J Cervantes","year":"2020","unstructured":"Cervantes, J., Garcia-Lamont, F., Rodr\u00edguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189\u2013215.","journal-title":"Neurocomputing"},{"key":"5400_CR14","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1016\/j.enpol.2013.10.042","volume":"65","author":"A Charles","year":"2014","unstructured":"Charles, A., & Darn\u00e9, O. (2014). Volatility persistence in crude oil markets. Energy Policy, 65, 729\u2013742.","journal-title":"Energy Policy"},{"key":"5400_CR15","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.eswa.2018.06.032","volume":"112","author":"SP Chatzis","year":"2018","unstructured":"Chatzis, S. P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., & Vlachogiannakis, N. (2018). Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Systems with Applications, 112, 353\u2013371.","journal-title":"Expert Systems with Applications"},{"key":"5400_CR16","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.procs.2017.11.373","volume":"122","author":"Y Chen","year":"2017","unstructured":"Chen, Y., He, K., & Tso, G. K. (2017). Forecasting crude oil prices: A deep learning based model. Procedia Computer Science, 122, 300\u2013307.","journal-title":"Procedia Computer Science"},{"key":"5400_CR17","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1016\/j.eneco.2017.12.035","volume":"78","author":"F Cheng","year":"2019","unstructured":"Cheng, F., Li, T., Wei, Y. m, & Fan, T. (2019). The vec-nar model for short-term forecasting of oil prices. Energy Economics, 78, 656\u2013667.","journal-title":"Energy Economics"},{"key":"5400_CR18","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1109\/72.279188","volume":"5","author":"JT Connor","year":"1994","unstructured":"Connor, J. T., Martin, R. D., & Atlas, L. E. (1994). Recurrent neural networks and robust time series prediction. IEEE Transactions on Neural Networks, 5, 240\u2013254.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"5400_CR19","doi-asserted-by":"publisher","first-page":"104978","DOI":"10.1016\/j.eneco.2020.104978","volume":"92","author":"S Corbet","year":"2020","unstructured":"Corbet, S., Goodell, J. W., & G\u00fcnay, S. (2020). Co-movements and spillovers of oil and renewable firms under extreme conditions: New evidence from negative wti prices during covid-19. Energy Economics, 92, 104978.","journal-title":"Energy Economics"},{"key":"5400_CR20","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273\u2013297.","journal-title":"Machine Learning"},{"key":"5400_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.121968","volume":"238","author":"RK de Medeiros","year":"2022","unstructured":"de Medeiros, R. K., da N\u00f3brega Besarria, C., de Jesus, D. P., & de Albuquerquemello, V. P. (2022). Forecasting oil prices: New approaches. Energy, 238, 121968.","journal-title":"Energy"},{"key":"5400_CR22","doi-asserted-by":"publisher","first-page":"157","DOI":"10.3390\/forecast1010011","volume":"1","author":"A Dimitriadou","year":"2019","unstructured":"Dimitriadou, A., Gogas, P., Papadimitriou, T., & Plakandaras, V. (2019). Oil market efficiency under a machine learning perspective. Forecasting, 1, 157\u2013168.","journal-title":"Forecasting"},{"key":"5400_CR23","doi-asserted-by":"publisher","first-page":"101816","DOI":"10.1016\/j.resourpol.2020.101816","volume":"69","author":"A Dutta","year":"2020","unstructured":"Dutta, A., Das, D., Jana, R., & Vo, X. V. (2020). Covid-19 and oil market crash: Revisiting the safe haven property of gold and bitcoin. Resources Policy, 69, 101816.","journal-title":"Resources Policy"},{"key":"5400_CR24","doi-asserted-by":"publisher","first-page":"108816","DOI":"10.1016\/j.ijheatfluidflow.2021.108816","volume":"90","author":"H Eivazi","year":"2021","unstructured":"Eivazi, H., Guastoni, L., Schlatter, P., Azizpour, H., & Vinuesa, R. (2021). Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence. International Journal of Heat and Fluid Flow, 90, 108816.","journal-title":"International Journal of Heat and Fluid Flow"},{"key":"5400_CR25","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1111\/j.1538-4616.2010.00323.x","volume":"42","author":"J Elder","year":"2010","unstructured":"Elder, J., & Serletis, A. (2010). Oil price uncertainty. Journal of Money, Credit and Banking, 42, 1137\u20131159.","journal-title":"Journal of Money, Credit and Banking"},{"key":"5400_CR26","unstructured":"Elsayed, S., Thyssens, D., Rashed, A., Jomaa, H.S., & Schmidt-Thieme, L. (2021). Do we really need deep learning models for time series forecasting? arXiv:2101.02118 ."},{"key":"5400_CR27","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1016\/j.asoc.2016.05.030","volume":"46","author":"E Esme","year":"2016","unstructured":"Esme, E., & Karlik, B. (2016). Fuzzy c-means based support vector machines classifier for perfume recognition. Applied Soft Computing, 46, 452\u2013458.","journal-title":"Applied Soft Computing"},{"key":"5400_CR28","doi-asserted-by":"publisher","first-page":"2448","DOI":"10.1016\/j.egypro.2019.01.318","volume":"158","author":"Y Feng","year":"2019","unstructured":"Feng, Y., Zhang, P., Yang, M., Li, Q., & Zhang, A. (2019). Short term load forecasting of offshore oil field microgrids based on da-svm. Energy Procedia, 158, 2448\u20132455.","journal-title":"Energy Procedia"},{"key":"5400_CR29","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.ejor.2017.11.054","volume":"270","author":"T Fischer","year":"2018","unstructured":"Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270, 654\u2013669.","journal-title":"European Journal of Operational Research"},{"key":"5400_CR30","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.pacfin.2018.09.005","volume":"53","author":"Z Ftiti","year":"2019","unstructured":"Ftiti, Z., & Hadhri, S. (2019). Can economic policy uncertainty, oil prices, and investor sentiment predict Islamic stock returns? a multi-scale perspective. Pacific-Basin Finance Journal, 53, 40\u201355.","journal-title":"Pacific-Basin Finance Journal"},{"key":"5400_CR31","doi-asserted-by":"crossref","unstructured":"Ftiti, Z., Tissaoui, K., & Boubaker, S. (2020). On the relationship between oil and gas markets: A new forecasting framework based on a machine learning approach. Annals of Operations Research, 1\u201329.","DOI":"10.1007\/s10479-020-03652-2"},{"key":"5400_CR32","doi-asserted-by":"publisher","first-page":"125188","DOI":"10.1016\/j.jhydrol.2020.125188","volume":"589","author":"S Gao","year":"2020","unstructured":"Gao, S., Huang, Y., Zhang, S., Han, J., Wang, G., Zhang, M., & Lin, Q. (2020). Short-term runoff prediction with gru and lstm networks without requiring time step optimization during sample generation. Journal of Hydrology, 589, 125188.","journal-title":"Journal of Hydrology"},{"key":"5400_CR33","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1023\/A:1010884214864","volume":"44","author":"CL Giles","year":"2001","unstructured":"Giles, C. L., Lawrence, S., & Tsoi, A. C. (2001). Noisy time series prediction using recurrent neural networks and grammatical inference. Machine Learning, 44, 161\u2013183.","journal-title":"Machine Learning"},{"key":"5400_CR34","doi-asserted-by":"publisher","first-page":"106678","DOI":"10.1016\/j.cie.2020.106678","volume":"147","author":"\u0130 G\u00fcven","year":"2020","unstructured":"G\u00fcven, \u0130, & \u015eim\u015fir, F. (2020). Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ann) and support vector machines (svm) methods. Computers & Industrial Engineering, 147, 106678.","journal-title":"Computers & Industrial Engineering"},{"key":"5400_CR35","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/S0304-4076(02)00207-5","volume":"113","author":"JD Hamilton","year":"2003","unstructured":"Hamilton, J. D. (2003). What is an oil shock? Journal of Econometrics, 113, 363\u2013398.","journal-title":"Journal of Econometrics"},{"key":"5400_CR36","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1016\/j.eswa.2006.06.009","volume":"33","author":"PY Hao","year":"2007","unstructured":"Hao, P. Y., Chiang, J. H., & Tu, Y. K. (2007). Hierarchically svm classification based on support vector clustering method and its application to document categorization. Expert Systems with Applications, 33, 627\u2013635.","journal-title":"Expert Systems with Applications"},{"key":"5400_CR37","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.energy.2019.04.077","volume":"179","author":"GP Herrera","year":"2019","unstructured":"Herrera, G. P., Constantino, M., Tabak, B. M., Pistori, H., Su, J. J., & Naranpanawa, A. (2019). Long-term forecast of energy commodities price using machine learning. Energy, 179, 214\u2013221.","journal-title":"Energy"},{"key":"5400_CR38","unstructured":"Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., Kolen, J., & Kremer, S. (2001). A field guide to dynamical recurrent neural networks. chapter Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term Dependencies , 237\u2013243."},{"key":"5400_CR39","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9, 1735\u20131780.","journal-title":"Neural Computation"},{"key":"5400_CR40","doi-asserted-by":"publisher","first-page":"124907","DOI":"10.1016\/j.physa.2020.124907","volume":"557","author":"Y Hu","year":"2020","unstructured":"Hu, Y., Ni, J., & Wen, L. (2020). A hybrid deep learning approach by integrating lstm-ann networks with garch model for copper price volatility prediction. Physica A: Statistical Mechanics and its Applications, 557, 124907.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"5400_CR41","unstructured":"IEA, U., (2020). Global energy review 2020. Ukraine.[Online] https:\/\/www.iea.org\/countries\/ukraine. Accessed 10, September 2020."},{"key":"5400_CR42","doi-asserted-by":"publisher","first-page":"13912","DOI":"10.46557\/001c.13912","volume":"1","author":"BN Iyke","year":"2020","unstructured":"Iyke, B. N. (2020). Covid-19: The reaction of us oil and gas producers to the pandemic. Energy Research Letters, 1, 13912.","journal-title":"Energy Research Letters"},{"key":"5400_CR43","doi-asserted-by":"publisher","first-page":"113511","DOI":"10.1016\/j.jenvman.2021.113511","volume":"298","author":"SB Jabeur","year":"2021","unstructured":"Jabeur, S. B., Khalfaoui, R., & Arfi, W. B. (2021). The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning. Journal of Environmental Management, 298, 113511.","journal-title":"Journal of Environmental Management"},{"key":"5400_CR44","doi-asserted-by":"crossref","unstructured":"Jawadi, F., Louhichi, W., Ameur, H.B., & Ftiti, Z. (2019). Do jumps and co-jumps improve volatility forecasting of oil and currency markets? The Energy Journal, 40.","DOI":"10.5547\/01956574.40.SI2.fjaw"},{"key":"5400_CR45","doi-asserted-by":"publisher","first-page":"101669","DOI":"10.1016\/j.erss.2020.101669","volume":"68","author":"M Jefferson","year":"2020","unstructured":"Jefferson, M. (2020). A crude future? Covid-19s challenges for oil demand, supply and prices. Energy Research & Social Science, 68, 101669.","journal-title":"Energy Research & Social Science"},{"key":"5400_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115537","volume":"184","author":"W Jiang","year":"2021","unstructured":"Jiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184, 115537.","journal-title":"Expert Systems with Applications"},{"key":"5400_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.123471","volume":"247","author":"Z Jiang","year":"2022","unstructured":"Jiang, Z., Zhang, L., Zhang, L., & Wen, B. (2022). Investor sentiment and machine learning: Predicting the price of China\u2019s crude oil futures market. Energy, 247, 123471.","journal-title":"Energy"},{"key":"5400_CR48","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.eneco.2008.09.006","volume":"31","author":"SH Kang","year":"2009","unstructured":"Kang, S. H., Kang, S. M., & Yoon, S. M. (2009). Forecasting volatility of crude oil markets. Energy Economics, 31, 119\u2013125.","journal-title":"Energy Economics"},{"key":"5400_CR49","first-page":"492","volume":"21","author":"P Kang-Lin","year":"2004","unstructured":"Kang-Lin, P., Wu, C. H., & Yeong-Jia, J. G. (2004). The development of a new statistical technique for relating financial information to stock market returns. International Journal of Management, 21, 492.","journal-title":"International Journal of Management"},{"key":"5400_CR50","doi-asserted-by":"crossref","unstructured":"Kim, K., & Aminanto, M.E. (2017). Deep learning in intrusion detection perspective: Overview and further challenges, In: 2017 International Workshop on Big Data and Information Security (IWBIS), IEEE. pp. 5\u201310.","DOI":"10.1109\/IWBIS.2017.8275095"},{"key":"5400_CR51","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.jhydrol.2015.12.014","volume":"534","author":"O Kisi","year":"2016","unstructured":"Kisi, O., & Parmar, K. S. (2016). Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. Journal of Hydrology, 534, 104\u2013112.","journal-title":"Journal of Hydrology"},{"key":"5400_CR52","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1007\/s11869-017-0477-9","volume":"10","author":"O Kisi","year":"2017","unstructured":"Kisi, O., Parmar, K. S., Soni, K., & Demir, V. (2017). Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and m5 model tree models. Air Quality, Atmosphere & Health, 10, 873\u2013883.","journal-title":"Air Quality, Atmosphere & Health"},{"key":"5400_CR53","doi-asserted-by":"publisher","first-page":"106952","DOI":"10.1016\/j.petrol.2020.106952","volume":"189","author":"K Li","year":"2020","unstructured":"Li, K., Zhou, G., Yang, Y., Li, F., & Jiao, Z. (2020). A novel prediction method for favorable reservoir of oil field based on grey wolf optimizer and twin support vector machine. Journal of Petroleum Science and Engineering, 189, 106952.","journal-title":"Journal of Petroleum Science and Engineering"},{"key":"5400_CR54","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1016\/j.ijforecast.2018.07.006","volume":"35","author":"X Li","year":"2019","unstructured":"Li, X., Shang, W., & Wang, S. (2019). Text-based crude oil price forecasting: A deep learning approach. International Journal of Forecasting, 35, 1548\u20131560.","journal-title":"International Journal of Forecasting"},{"key":"5400_CR55","unstructured":"Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv:1506.00019."},{"key":"5400_CR56","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.physa.2014.07.007","volume":"413","author":"L Liu","year":"2014","unstructured":"Liu, L., & Ma, G. (2014). Cross-correlation between crude oil and refined product prices. Physica A: Statistical Mechanics and its Applications, 413, 284\u2013293.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"5400_CR57","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1016\/0169-2070(93)90079-3","volume":"9","author":"S Makridakis","year":"1993","unstructured":"Makridakis, S. (1993). Accuracy measures: Theoretical and practical concerns. International Journal of Forecasting, 9, 527\u2013529.","journal-title":"International Journal of Forecasting"},{"key":"5400_CR58","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.econlet.2015.11.035","volume":"139","author":"E Malikov","year":"2016","unstructured":"Malikov, E. (2016). Dynamic responses to oil price shocks: Conditional vs unconditional (a) symmetry. Economics Letters, 139, 31\u201335.","journal-title":"Economics Letters"},{"key":"5400_CR59","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.econlet.2011.08.010","volume":"113","author":"J McKenzie","year":"2011","unstructured":"McKenzie, J. (2011). Mean absolute percentage error and bias in economic forecasting. Economics Letters, 113, 259\u2013262.","journal-title":"Economics Letters"},{"key":"5400_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.najef.2021.101446","volume":"57","author":"W Mensi","year":"2021","unstructured":"Mensi, W., Lee, Y. J., Vo, X. V., & Yoon, S. M. (2021). Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil consumers? evidence from an asymmetric mf-dfa approach. The North American Journal of Economics and Finance, 57, 101446.","journal-title":"The North American Journal of Economics and Finance"},{"key":"5400_CR61","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.irfa.2015.01.011","volume":"39","author":"PK Narayan","year":"2015","unstructured":"Narayan, P. K., & Sharma, S. S. (2015). Does data frequency matter for the impact of forward premium on spot exchange rate? International Review of Financial Analysis, 39, 45\u201353.","journal-title":"International Review of Financial Analysis"},{"key":"5400_CR62","doi-asserted-by":"publisher","first-page":"113237","DOI":"10.1016\/j.eswa.2020.113237","volume":"148","author":"T Niu","year":"2020","unstructured":"Niu, T., Wang, J., Lu, H., Yang, W., & Du, P. (2020). Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting. Expert Systems with Applications, 148, 113237.","journal-title":"Expert Systems with Applications"},{"key":"5400_CR63","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1038\/nbt1206-1565","volume":"24","author":"WS Noble","year":"2006","unstructured":"Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24, 1565\u20131567.","journal-title":"Nature Biotechnology"},{"key":"5400_CR64","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1016\/j.resourpol.2018.10.008","volume":"62","author":"H Nourali","year":"2019","unstructured":"Nourali, H., & Osanloo, M. (2019). Mining capital cost estimation using support vector regression (svr). Resources Policy, 62, 527\u2013540.","journal-title":"Resources Policy"},{"key":"5400_CR65","doi-asserted-by":"publisher","first-page":"10235","DOI":"10.1002\/er.6512","volume":"45","author":"OE Olubusoye","year":"2021","unstructured":"Olubusoye, O. E., Ogbonna, A. E., Yaya, O. S., & Umolo, D. (2021). An information-based index of uncertainty and the predictability of energy prices. International Journal of Energy Research, 45, 10235\u201310249.","journal-title":"International Journal of Energy Research"},{"key":"5400_CR66","doi-asserted-by":"publisher","first-page":"110018","DOI":"10.1016\/j.chaos.2020.110018","volume":"138","author":"RK Pathan","year":"2020","unstructured":"Pathan, R. K., Biswas, M., & Khandaker, M. U. (2020). Time series prediction of covid-19 by mutation rate analysis using recurrent neural network-based lstm model. Chaos, Solitons & Fractals, 138, 110018.","journal-title":"Chaos, Solitons & Fractals"},{"key":"5400_CR67","doi-asserted-by":"publisher","first-page":"13166","DOI":"10.46557\/001c.13166","volume":"1","author":"M Qin","year":"2020","unstructured":"Qin, M., Zhang, Y. C., & Su, C. W. (2020). The essential role of pandemics: A fresh insight into the oil market. Energy Research Letters, 1, 13166.","journal-title":"Energy Research Letters"},{"key":"5400_CR68","unstructured":"Reddy, K. S. S., Reddy, Y. P., & Rao, C. M. (2020). Recurrent neural network based prediction of number of covid-19 cases in India. Materials Today: Proceedings."},{"key":"5400_CR69","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.physa.2016.02.018","volume":"453","author":"Q Ruan","year":"2016","unstructured":"Ruan, Q., Wang, Y., Lu, X., & Qin, J. (2016). Cross-correlations between Baltic dry index and crude oil prices. Physica A: Statistical Mechanics and its Applications, 453, 278\u2013289.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"5400_CR70","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","volume":"323","author":"A Sagheer","year":"2019","unstructured":"Sagheer, A., & Kotb, M. (2019). Time series forecasting of petroleum production using deep lstm recurrent networks. Neurocomputing, 323, 203\u2013213.","journal-title":"Neurocomputing"},{"key":"5400_CR71","doi-asserted-by":"publisher","first-page":"104450","DOI":"10.1016\/j.catena.2019.104450","volume":"189","author":"M Sahana","year":"2020","unstructured":"Sahana, M., Rehman, S., Sajjad, H., & Hong, H. (2020). Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of sundarban biosphere reserve, india. Catena, 189, 104450.","journal-title":"Catena"},{"key":"5400_CR72","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.iref.2020.06.023","volume":"69","author":"AA Salisu","year":"2020","unstructured":"Salisu, A. A., Ebuh, G. U., & Usman, N. (2020). Revisiting oil-stock nexus during covid-19 pandemic: Some preliminary results. International Review of Economics & Finance, 69, 280\u2013294.","journal-title":"International Review of Economics & Finance"},{"key":"5400_CR73","doi-asserted-by":"publisher","first-page":"554","DOI":"10.1016\/j.enpol.2012.10.003","volume":"52","author":"AA Salisu","year":"2013","unstructured":"Salisu, A. A., & Fasanya, I. O. (2013). Modelling oil price volatility with structural breaks. Energy Policy, 52, 554\u2013562.","journal-title":"Energy Policy"},{"key":"5400_CR74","doi-asserted-by":"publisher","first-page":"102508","DOI":"10.1016\/j.resourpol.2021.102508","volume":"75","author":"AA Salisu","year":"2022","unstructured":"Salisu, A. A., Gupta, R., & Ji, Q. (2022). Forecasting oil prices over 150 years: The role of tail risks. Resources Policy, 75, 102508.","journal-title":"Resources Policy"},{"key":"5400_CR75","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":"5400_CR76","doi-asserted-by":"crossref","unstructured":"Shawe-Taylor, J., & Cristianini, N. (2000). An introduction to support vector machines and other kernel-based learning methods, Vol. 204.","DOI":"10.1017\/CBO9780511801389"},{"key":"5400_CR77","doi-asserted-by":"crossref","unstructured":"Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2019). The performance of lstm and bilstm in forecasting time series. In 2019 IEEE International Conference on Big Data (Big Data), IEEE (pp. 3285\u20133292).","DOI":"10.1109\/BigData47090.2019.9005997"},{"key":"5400_CR78","doi-asserted-by":"publisher","first-page":"110086","DOI":"10.1016\/j.chaos.2020.110086","volume":"139","author":"S Singh","year":"2020","unstructured":"Singh, S., Parmar, K. S., Makkhan, S. J. S., Kaur, J., Peshoria, S., & Kumar, J. (2020). Study of arima and least square support vector machine (ls-svm) models for the prediction of sars-cov-2 confirmed cases in the most affected countries. Chaos, Solitons & Fractals, 139, 110086.","journal-title":"Chaos, Solitons & Fractals"},{"key":"5400_CR79","doi-asserted-by":"crossref","unstructured":"Sun, Q., Tang, Z., Gao, J., & Zhang, G. (2021). Short-term ship motion attitude prediction based on lstm and gpr. Applied Ocean Research, 102927.","DOI":"10.1016\/j.apor.2021.102927"},{"key":"5400_CR80","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.patrec.2019.10.034","volume":"128","author":"D Thara","year":"2019","unstructured":"Thara, D., PremaSudha, B., & Xiong, F. (2019). Epileptic seizure detection and prediction using stacked bidirectional long short term memory. Pattern Recognition Letters, 128, 529\u2013535.","journal-title":"Pattern Recognition Letters"},{"key":"5400_CR81","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1109\/72.788640","volume":"10","author":"VN Vapnik","year":"1999","unstructured":"Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10, 988\u2013999.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"5400_CR82","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1016\/j.neucom.2006.10.021","volume":"70","author":"H Wang","year":"2007","unstructured":"Wang, H., Pi, D., & Sun, Y. (2007). Online svm regression algorithm-based adaptive inverse control. Neurocomputing, 70, 952\u2013959.","journal-title":"Neurocomputing"},{"key":"5400_CR83","doi-asserted-by":"publisher","first-page":"119492","DOI":"10.1016\/j.jclepro.2019.119492","volume":"250","author":"L Wen","year":"2020","unstructured":"Wen, L., & Cao, Y. (2020). Influencing factors analysis and forecasting of residential energy-related co2 emissions utilizing optimized support vector machine. Journal of Cleaner Production, 250, 119492.","journal-title":"Journal of Cleaner Production"},{"key":"5400_CR84","unstructured":"Weninger, F., Bergmann, J., & Schuller, B. (2015). Introducing currennt: The munich open-source cuda recurrent neural network toolkit. Journal of Machine Learning Research, 547\u2013551."},{"key":"5400_CR85","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.nima.2017.06.020","volume":"867","author":"M Wielgosz","year":"2017","unstructured":"Wielgosz, M., Skocze\u0144, A., & Mertik, M. (2017). Using lstm recurrent neural networks for monitoring the lhc superconducting magnets. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 867, 40\u201350.","journal-title":"Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment"},{"key":"5400_CR86","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1016\/j.cie.2010.07.019","volume":"59","author":"CC Yang","year":"2010","unstructured":"Yang, C. C., & Shieh, M. D. (2010). A support vector regression based prediction model of affective responses for product form design. Computers & Industrial Engineering, 59, 682\u2013689.","journal-title":"Computers & Industrial Engineering"},{"key":"5400_CR87","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.chaos.2004.11.015","volume":"25","author":"S Yousefi","year":"2005","unstructured":"Yousefi, S., Weinreich, I., & Reinarz, D. (2005). Wavelet-based prediction of oil prices. Chaos, Solitons & Fractals, 25, 265\u2013275.","journal-title":"Chaos, Solitons & Fractals"},{"key":"5400_CR88","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.jhydrol.2017.06.020","volume":"552","author":"PS Yu","year":"2017","unstructured":"Yu, P. S., Yang, T. C., Chen, S. Y., Kuo, C. M., & Tseng, H. W. (2017). Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting. Journal of Hydrology, 552, 92\u2013104.","journal-title":"Journal of Hydrology"},{"key":"5400_CR89","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: Lstm cells and network architectures. Neural Computation, 31, 1235\u20131270.","journal-title":"Neural Computation"},{"key":"5400_CR90","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1016\/j.jhydrol.2018.04.065","volume":"561","author":"J Zhang","year":"2018","unstructured":"Zhang, J., Zhu, Y., Zhang, X., Ye, M., & Yang, J. (2018). Developing a long short-term memory (lstm) based model for predicting water table depth in agricultural areas. Journal of Hydrology, 561, 918\u2013929.","journal-title":"Journal of Hydrology"},{"key":"5400_CR91","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1016\/j.eneco.2015.02.018","volume":"49","author":"JL Zhang","year":"2015","unstructured":"Zhang, J. L., Zhang, Y. J., & Zhang, L. (2015). A novel hybrid method for crude oil price forecasting. Energy Economics, 49, 649\u2013659.","journal-title":"Energy Economics"},{"key":"5400_CR92","doi-asserted-by":"publisher","first-page":"101702","DOI":"10.1016\/j.irfa.2021.101702","volume":"74","author":"W Zhang","year":"2021","unstructured":"Zhang, W., & Hamori, S. (2021). Crude oil market and stock markets during the covid-19 pandemic: Evidence from the US, Japan, and Germany. International Review of Financial Analysis, 74, 101702.","journal-title":"International Review of Financial Analysis"},{"key":"5400_CR93","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.epsr.2017.01.035","volume":"146","author":"X Zhang","year":"2017","unstructured":"Zhang, X., Wang, J., & Zhang, K. (2017). Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by cuckoo search algorithm. Electric Power Systems Research, 146, 270\u2013285.","journal-title":"Electric Power Systems Research"},{"key":"5400_CR94","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1049\/iet-its.2016.0208","volume":"11","author":"Z Zhao","year":"2017","unstructured":"Zhao, Z., Chen, W., Wu, X., Chen, P. C., & Liu, J. (2017). Lstm network: A deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, 11, 68\u201375.","journal-title":"IET Intelligent Transport Systems"}],"container-title":["Annals of Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-023-05400-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10479-023-05400-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10479-023-05400-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T15:52:41Z","timestamp":1740585161000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10479-023-05400-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,12]]},"references-count":94,"journal-issue":{"issue":"2-3","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["5400"],"URL":"https:\/\/doi.org\/10.1007\/s10479-023-05400-8","relation":{},"ISSN":["0254-5330","1572-9338"],"issn-type":[{"value":"0254-5330","type":"print"},{"value":"1572-9338","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,12]]},"assertion":[{"value":"24 March 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}