{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T10:30:08Z","timestamp":1776940208389,"version":"3.51.4"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031790409","type":"print"},{"value":"9783031790416","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-79041-6_1","type":"book-chapter","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T11:47:36Z","timestamp":1738324056000},"page":"3-16","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Crude Oil Price Forecasting Using Hybridization of Optimized Deep Learning and Shallow Machine Learning Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3343-6818","authenticated-orcid":false,"given":"Sourav Kumar","family":"Purohit","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1409-3856","authenticated-orcid":false,"given":"Sibarama","family":"Panigrahi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditya Narayan","family":"Jena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,1]]},"reference":[{"issue":"4","key":"1_CR1","doi-asserted-by":"publisher","first-page":"3173","DOI":"10.22214\/ijraset.2023.50851","volume":"11","author":"A Periwal","year":"2023","unstructured":"Periwal, A.: The impact of COP fluctuations on Indian economy. Int. J. Res. Appl. Sci. Eng. Technol. 11(4), 3173\u20133202 (2023)","journal-title":"Int. J. Res. Appl. Sci. Eng. Technol."},{"issue":"2","key":"1_CR2","doi-asserted-by":"publisher","first-page":"405","DOI":"10.29207\/resti.v7i2.4895","volume":"7","author":"VP Ariyanti","year":"2023","unstructured":"Ariyanti, V.P., Yusnitasari, T.: Comparison of Arima and Sarima for forecasting COPs. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7(2), 405\u2013413 (2023)","journal-title":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)"},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"238","DOI":"10.54097\/hset.v39i.6535","volume":"39","author":"Y Wang","year":"2023","unstructured":"Wang, Y.: Oil Price Forecasting based on Improved SARIMA Model. Highl. Sci. Eng. Technol. 39, 238\u2013245 (2023)","journal-title":"Highl. Sci. Eng. Technol."},{"issue":"1","key":"1_CR4","doi-asserted-by":"publisher","first-page":"197","DOI":"10.14254\/1800-5845\/2021.17-1.15","volume":"17","author":"MI Haque","year":"2021","unstructured":"Haque, M.I., Shaik, A.R.: Predicting COPs during a pandemic: a comparison of arima and garch models. Montenegrin J. Econ. 17(1), 197\u2013207 (2021)","journal-title":"Montenegrin J. Econ."},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Purohit, S.K., Panigrahi, S.: Forecasting COPs: a machine learning perspective. In: International Conference on Computing, Communication and Learning, pp. 15\u201326. Cham: Springer Nature Switzerland (2023)","DOI":"10.1007\/978-3-031-56998-2_2"},{"issue":"1","key":"1_CR6","first-page":"70","volume":"10","author":"EG Okoro","year":"2014","unstructured":"Okoro, E.G.: Oil price volatility and economic growth in Nigeria: a Vector Auto-Regression (VAR) approach. Acta Universitatis Danubius. OEconomica 10(1), 70\u201382 (2014)","journal-title":"Acta Universitatis Danubius. OEconomica"},{"issue":"1","key":"1_CR7","doi-asserted-by":"publisher","first-page":"127","DOI":"10.29207\/resti.v8i1.5551","volume":"8","author":"D Suryani","year":"2024","unstructured":"Suryani, D., Fadhila, M.: Indonesian COP (ICP) prediction using support vector regression algorithm. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8(1), 127\u2013135 (2024)","journal-title":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)"},{"issue":"8","key":"1_CR8","doi-asserted-by":"publisher","first-page":"598","DOI":"10.3390\/fractalfract7080598","volume":"7","author":"YB \u00d6z\u00e7elik","year":"2023","unstructured":"\u00d6z\u00e7elik, Y.B., Altan, A.: Overcoming nonlinear dynamics in diabetic retinopathy classification: a robust AI-based model with chaotic swarm intelligence optimization and recurrent long short-term memory. Fractal Fract. 7(8), 598 (2023)","journal-title":"Fractal Fract."},{"key":"1_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.123360","volume":"541","author":"Y Zou","year":"2020","unstructured":"Zou, Y., Yu, L., Tso, G.K., He, K.: Risk forecasting in the crude oil market: a multiscale Convolutional Neural Network approach. Physica A 541, 123360 (2020)","journal-title":"Physica A"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Pulimoottil, J.S., Kaushik, J.: A study on COP forecasting using RNN model. In: Data Science and Security: Proceedings of IDSCS 2022, pp. 423\u2013432. Singapore: Springer Nature Singapore (2022)","DOI":"10.1007\/978-981-19-2211-4_38"},{"key":"1_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.122964","volume":"242","author":"S Karasu","year":"2022","unstructured":"Karasu, S., Altan, A.: Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization. Energy 242, 122964 (2022)","journal-title":"Energy"},{"key":"1_CR12","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.: Investor sentiment and machine learning: predicting the price of China\u2019s crude oil futures market. Energy 247, 123471 (2022)","journal-title":"Energy"},{"key":"1_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120963","volume":"231","author":"S Urolagin","year":"2021","unstructured":"Urolagin, S., Sharma, N., Datta, T.K.: A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting. Energy 231, 120963 (2021)","journal-title":"Energy"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Li, Y., Jiang, S., Li, X., Wang, S.: The role of news sentiment in oil futures returns and volatility forecasting: data-decomposition based deep learning approach. Energy Econ. 105140 (2021)","DOI":"10.1016\/j.eneco.2021.105140"},{"key":"1_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compchemeng.2021.107513","volume":"155","author":"GA Busari","year":"2021","unstructured":"Busari, G.A., Lim, D.H.: COP prediction: a comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance. Comput. Chem. Eng. 155, 107513 (2021)","journal-title":"Comput. Chem. Eng."},{"key":"1_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119617","volume":"218","author":"S Zhang","year":"2023","unstructured":"Zhang, S., Luo, J., Wang, S., Liu, F.: Oil price forecasting: a hybrid GRU neural network based on decomposition\u2013reconstruction methods. Expert Syst. Appl. 218, 119617 (2023)","journal-title":"Expert Syst. Appl."},{"key":"1_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eneco.2020.104827","volume":"90","author":"B Wang","year":"2020","unstructured":"Wang, B., Wang, J.: Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation. Energy Econ. 90, 104827 (2020)","journal-title":"Energy Econ."},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Gulati, K., Gupta, J., Rani, L., kumar Sarangi, P.: Crude oil prices predictions in India using machine learning based hybrid model. In:\u00a02022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO),\u00a0pp. 1\u20136. IEEE","DOI":"10.1109\/ICRITO56286.2022.9964577"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Govind, G.R., Babu, A.S.: Hybrid deep learning model to forecast COP. In: 2023 International Conference on Inventive Computation Technologies (ICICT), pp. 19\u201323. IEEE (2023)","DOI":"10.1109\/ICICT57646.2023.10134438"},{"issue":"10","key":"1_CR20","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.13449","volume":"40","author":"SS Pradhan","year":"2023","unstructured":"Pradhan, S.S., Panigrahi, S., Purohit, S.K., Dash, J.K.: Study and development of hybrid and ensemble forecasting models for air quality index forecasting. Expert. Syst. 40(10), e13449 (2023)","journal-title":"Expert. Syst."},{"issue":"11","key":"1_CR21","doi-asserted-by":"publisher","first-page":"2521","DOI":"10.3390\/electronics12112521","volume":"12","author":"J Wang","year":"2023","unstructured":"Wang, J., Zhang, T., Lu, T., Xue, Z.: A hybrid forecast model of EEMD-CNN-ILSTM for crude oil futures price. Electronics 12(11), 2521 (2023)","journal-title":"Electronics"},{"key":"1_CR22","doi-asserted-by":"publisher","unstructured":"Shahbazbegian, V., Hosseininesaz, H., Shafie-Khah M., Elmusrati, M.: Forecasting crude oil prices using a hybrid model combining long short-term memory neural networks and markov switching model. In: International Conference on Future Energy Solutions (FES), Vaasa, Finland, 1\u20136, https:\/\/doi.org\/10.1109\/FES57669.2023.10182444","DOI":"10.1109\/FES57669.2023.10182444"},{"key":"1_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113686","volume":"161","author":"B Wang","year":"2020","unstructured":"Wang, B., Wang, J.: Deep multi-hybrid forecasting system with random EWT extraction and variational learning rate algorithm for crude oil futures. Expert Syst. Appl. 161, 113686 (2020)","journal-title":"Expert Syst. Appl."},{"key":"1_CR24","doi-asserted-by":"publisher","first-page":"149067","DOI":"10.1109\/ACCESS.2021.3124802","volume":"9","author":"J Liu","year":"2021","unstructured":"Liu, J., Huang, X.: Forecasting COP using event extraction. IEEE Access 9, 149067\u2013149076 (2021)","journal-title":"IEEE Access"},{"issue":"11","key":"1_CR25","doi-asserted-by":"publisher","first-page":"3029","DOI":"10.3390\/en11113029","volume":"11","author":"SC Huang","year":"2018","unstructured":"Huang, S.C., Wu, C.F.: Energy commodity price forecasting with deep multiple kernel learning. Energies 11(11), 3029 (2018)","journal-title":"Energies"},{"issue":"1","key":"1_CR26","first-page":"1","volume":"13","author":"M Ofuoku","year":"2022","unstructured":"Ofuoku, M., Ngniatedema, T.: Predicting the price of crude palm oil: a deep learning approach. Int. J. Strat. Dec. Sci. (IJSDS) 13(1), 1\u201315 (2022)","journal-title":"Int. J. Strat. Dec. Sci. (IJSDS)"},{"issue":"2","key":"1_CR27","first-page":"302","volume":"14","author":"KA Kakade","year":"2023","unstructured":"Kakade, K.A., Ghate, K.S., Jaiswal, R.K., Jaiswal, R.: A novel approach to forecast crude oil prices using machine learning and technical indicators. J. Adv. Inf. Technol. 14(2), 302\u2013310 (2023)","journal-title":"J. Adv. Inf. Technol."},{"key":"1_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106509","volume":"95","author":"J Wang","year":"2020","unstructured":"Wang, J., Niu, T., Du, P., Yang, W.: Ensemble interval prediction approach for modeling uncertainty in COP. Appl. Soft Comput. 95, 106509 (2020)","journal-title":"Appl. Soft Comput."},{"key":"1_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.120021","volume":"658","author":"SK Purohit","year":"2024","unstructured":"Purohit, S.K., Panigrahi, S.: Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models. Inf. Sci. 658, 120021 (2024)","journal-title":"Inf. Sci."},{"issue":"15","key":"1_CR30","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1080\/08839514.2021.1981659","volume":"35","author":"SK Purohit","year":"2021","unstructured":"Purohit, S.K., Panigrahi, S., Sethy, P.K., Behera, S.K.: Time series forecasting of price of agricultural products using hybrid methods. Appl. Artif. Intell. 35(15), 1388\u20131406 (2021)","journal-title":"Appl. Artif. Intell."},{"issue":"4","key":"1_CR31","doi-asserted-by":"publisher","first-page":"2929","DOI":"10.1007\/s11277-023-10265-y","volume":"129","author":"K Das","year":"2023","unstructured":"Das, K., Das, S., Panigrahi, S.: Energy-efficient forecasting of temperature data in sensor cloud system using a hybrid SVM-ANN method. Wirel. Pers. Commun. 129(4), 2929\u20132944 (2023)","journal-title":"Wirel. Pers. Commun."},{"issue":"12","key":"1_CR32","doi-asserted-by":"publisher","first-page":"11129","DOI":"10.1007\/s13369-020-05004-5","volume":"45","author":"S Panigrahi","year":"2020","unstructured":"Panigrahi, S., Behera, H.S.: Time series forecasting using differential evolution-based ANN modelling scheme. Arab. J. Sci. Eng. 45(12), 11129\u201311146 (2020)","journal-title":"Arab. J. Sci. Eng."},{"issue":"1\u20132","key":"1_CR33","first-page":"4","volume":"8","author":"S Panigrahi","year":"2019","unstructured":"Panigrahi, S., Behera, H.S.: Nonlinear time series forecasting using a novel self-adaptive TLBO-MFLANN model. Int. J. Comput. Intell. Stud. 8(1\u20132), 4\u201326 (2019)","journal-title":"Int. J. Comput. Intell. Stud."},{"key":"1_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/for.3153","volume":"43","author":"SS Pradhan","year":"2024","unstructured":"Pradhan, S.S., Panigrahi, S.: A study and development of high-order fuzzy time series forecasting methods for air quality index forecasting. J. Forecast. 43, 1\u201324 (2024). https:\/\/doi.org\/10.1002\/for.3153","journal-title":"J. Forecast."},{"key":"1_CR35","doi-asserted-by":"crossref","unstructured":"Panigrahi, S., Behera, H.S.: Fuzzy time series forecasting: a survey. In:\u00a0Computational Intelligence in Data Mining: Proceedings of the International Conference on ICCIDM 2018,\u00a0pp. 641\u2013651. Springer Singapore (2020)","DOI":"10.1007\/978-981-13-8676-3_54"},{"key":"1_CR36","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.ins.2023.01.075","volume":"626","author":"RM Pattanayak","year":"2023","unstructured":"Pattanayak, R.M., Behera, H.S., Panigrahi, S.: A novel high order hesitant fuzzy time series forecasting by using mean aggregated membership value with support vector machine. Inf. Sci. 626, 494\u2013523 (2023)","journal-title":"Inf. Sci."},{"issue":"2","key":"1_CR37","doi-asserted-by":"publisher","first-page":"1399","DOI":"10.1007\/s13369-021-05718-0","volume":"47","author":"RM Pattanayak","year":"2022","unstructured":"Pattanayak, R.M., Behera, H.S., Panigrahi, S.: A non-probabilistic neutrosophic entropy-based method for high-order fuzzy time-series forecasting. Arab. J. Sci. Eng. 47(2), 1399\u20131421 (2022)","journal-title":"Arab. J. Sci. Eng."},{"key":"1_CR38","first-page":"7215","volume":"96","author":"S Panigrahi","year":"2018","unstructured":"Panigrahi, S., Behera, H.S.: A computationally efficient method for high order fuzzy time series forecasting. J. Theor. Appl. Inf. Technol. 96, 7215\u20137226 (2018)","journal-title":"J. Theor. Appl. Inf. Technol."}],"container-title":["Communications in Computer and Information Science","Computing, Communication and Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-79041-6_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T11:47:45Z","timestamp":1738324065000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-79041-6_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031790409","9783031790416"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-79041-6_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CoCoLe","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computing, Communication and Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Warangal","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cocole2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ic-cocole.in\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}