{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T18:16:39Z","timestamp":1778782599503,"version":"3.51.4"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52502411"],"award-info":[{"award-number":["52502411"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.eswa.2026.132446","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T06:17:36Z","timestamp":1776233856000},"page":"132446","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Bridging Discontinuities: A Large Language Model Augmented Uncertainty-Aware Framework for Short-Term Petrochemical Price Forecasting"],"prefix":"10.1016","volume":"323","author":[{"given":"Haochong","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinwei","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siliang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1248-6155","authenticated-orcid":false,"given":"Rui","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongtu","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132446_b0005","doi-asserted-by":"crossref","first-page":"20939","DOI":"10.1007\/s00521-022-07571-0","article-title":"Dynamic evolutionary data and text document clustering approach using improved Aquila optimizer based arithmetic optimization algorithm and differential evolution","volume":"34","author":"Abualigah","year":"2022","journal-title":"Neural Computing and Applications"},{"key":"10.1016\/j.eswa.2026.132446_b0010","doi-asserted-by":"crossref","first-page":"e2067","DOI":"10.7717\/peerj-cs.2067","article-title":"Reliable renewable energy forecasting for climate change mitigation","volume":"10","author":"Atwa","year":"2024","journal-title":"PeerJ Computer Science"},{"key":"10.1016\/j.eswa.2026.132446_b0015","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1002\/mpr.329","article-title":"Multiple imputation by chained equations: What is it and how does it work?","volume":"20","author":"Azur","year":"2011","journal-title":"International journal of methods in psychiatric research"},{"key":"10.1016\/j.eswa.2026.132446_b0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121973","article-title":"Miipw: An r package for generalized estimating equations with missing data integration using a combination of mean score and inverse probability weighted approaches and multiple imputation","volume":"238","author":"Bhattacharjee","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132446_b0030","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1016\/j.apenergy.2019.05.068","article-title":"Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices","volume":"250","author":"Brusaferri","year":"2019","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2026.132446_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2023.117715","article-title":"Regional wind-photovoltaic combined power generation forecasting based on a novel multi-task learning framework and TPA-LSTM","volume":"297","author":"Chen","year":"2023","journal-title":"Energy Conversion and Management"},{"key":"10.1016\/j.eswa.2026.132446_b0040","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.dsm.2024.10.003","article-title":"Probabilistic oil price forecasting with a variational mode decomposition-gated recurrent unit model incorporating pinball loss","volume":"8","author":"Cui","year":"2025","journal-title":"Data Science and Management"},{"key":"10.1016\/j.eswa.2026.132446_b0045","article-title":"A \u201cparallel\u201d combined Transformer-CNN model using secondary decomposition for crude oil forecasting","volume":"299","author":"Dai","year":"2025","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.1016\/j.eswa.2026.132446_b0050","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1038\/s44387-025-00054-2","article-title":"Towards parameter identification in pipeline hydraulics: integrating data-driven discovery and knowledge embedding","volume":"2","author":"Du","year":"2026","journal-title":"npj Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132446_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.125976","article-title":"A knowledge-enhanced graph-based temporal-spatial network for natural gas consumption prediction","volume":"263","author":"Du","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2026.132446_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105647","article-title":"A theory-guided deep-learning method for predicting power generation of multi-region photovoltaic plants","volume":"118","author":"Du","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132446_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.124689","article-title":"A hybrid deep learning framework for predicting daily natural gas consumption","volume":"257","author":"Du","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2026.132446_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.123968","article-title":"A novel dynamic time-delay grey model of energy prices and its application in crude oil price forecasting","volume":"251","author":"Duan","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2026.132446_b0075","first-page":"1493","article-title":"Locally adaptive factor processes for multivariate time series","volume":"15","author":"Durante","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2026.132446_b0080","series-title":"MRC-LSTM: A hybrid approach of multi-scale residual CNN and LSTM to predict bitcoin price.","first-page":"1","author":"Guo","year":"2021"},{"key":"10.1016\/j.eswa.2026.132446_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2020.117893","article-title":"Energy efficiency evaluation of complex petrochemical industries","volume":"203","author":"Han","year":"2020","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2026.132446_b9000","doi-asserted-by":"crossref","first-page":"3004","DOI":"10.1016\/j.petsci.2022.05.002","article-title":"A liquid loading prediction method of gas pipeline based on machine learning","volume":"19","author":"Hong","year":"2022","journal-title":"Petroleum Science"},{"key":"10.1016\/j.eswa.2026.132446_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.123497","article-title":"Conformalized temporal convolutional quantile regression networks for wind power interval forecasting","volume":"248","author":"Hu","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2026.132446_b0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.131185","article-title":"Multivariate analysis and forecasting of the crude oil prices: Part I\u2013Classical machine learning approaches","volume":"296","author":"Jha","year":"2024","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2026.132446_b0100","doi-asserted-by":"crossref","first-page":"11610","DOI":"10.1021\/acs.iecr.0c01957","article-title":"Comprehensive decision framework combining price prediction and production-planning models for strategic operation of a petrochemical industry","volume":"59","author":"Kwon","year":"2020","journal-title":"Industrial & Engineering Chemistry Research"},{"key":"10.1016\/j.eswa.2026.132446_b0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2021.101443","article-title":"Data science and reinforcement learning for price forecasting and raw material procurement in petrochemical industry","volume":"51","author":"Lee","year":"2022","journal-title":"Advanced Engineering Informatics"},{"key":"10.1016\/j.eswa.2026.132446_b0110","first-page":"1","article-title":"Enhanced Outbound volume Prediction for Refined Oil Depots via an innovative Multitask Learning Framework","author":"Li","year":"2026","journal-title":"SPE Journal"},{"key":"10.1016\/j.eswa.2026.132446_b0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124217","article-title":"KNN-GNN: A powerful graph neural network enhanced by aggregating K-nearest neighbors in common subspace","volume":"253","author":"Li","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132446_b0120","unstructured":"Liu, Y., Hu, T., Zhang, H., Wu, H., Wang, S., Ma, L., & Long, M. (2023). itransformer: Inverted transformers are effective for time series forecasting. arXiv preprint arXiv:2310.06625."},{"key":"10.1016\/j.eswa.2026.132446_b9005","series-title":"In 2025 10th International Conference on Computer and Information Processing Technology (ISCIPT)","first-page":"86","article-title":"Ct-patchtst: Channel-time patch time-series transformer for long-term renewable energy forecasting","author":"Lu","year":"2025"},{"key":"10.1016\/j.eswa.2026.132446_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2023.e21439","article-title":"Incorporating Russo-Ukrainian war in Brent crude oil price forecasting: A comparative analysis of ARIMA. TARMA and ENNReg models","volume":"9","author":"Mati","year":"2023","journal-title":"Heliyon"},{"key":"10.1016\/j.eswa.2026.132446_b0135","doi-asserted-by":"crossref","DOI":"10.1080\/23322039.2023.2213876","article-title":"Day-of-the-week effect: Petroleum and petroleum products","volume":"11","author":"Meek","year":"2023","journal-title":"Cogent Economics & Finance"},{"key":"10.1016\/j.eswa.2026.132446_b0140","first-page":"155","article-title":"The relationship between global economy and the effect on export pricing strategy of Petrochemical products","volume":"15","author":"Mostajerian","year":"2024","journal-title":"International Journal of Nonlinear Analysis and Applications"},{"key":"10.1016\/j.eswa.2026.132446_b0145","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.petrol.2018.09.031","article-title":"Novel statistical forecasting models for crude oil price, gas price, and interest rate based on meta-heuristic bat algorithm","volume":"172","author":"Naderi","year":"2019","journal-title":"Journal of Petroleum Science and Engineering"},{"key":"10.1016\/j.eswa.2026.132446_b0150","doi-asserted-by":"crossref","first-page":"278","DOI":"10.4028\/www.scientific.net\/MSF.803.278","article-title":"Comparison of linear interpolation method and mean method to replace the missing values in environmental data set","volume":"803","author":"Noor","year":"2015","journal-title":"Materials Science Forum"},{"key":"10.1016\/j.eswa.2026.132446_b0155","first-page":"3861","article-title":"Roles of imputation methods for filling the missing values: A review","volume":"7","author":"Ramli","year":"2013","journal-title":"Advances in Environmental Biology"},{"key":"10.1016\/j.eswa.2026.132446_b0160","unstructured":"Romano, Y., Patterson, E., & Candes, E. (2019). Conformalized quantile regression. Advances in neural information processing systems, 32."},{"key":"10.1016\/j.eswa.2026.132446_b0165","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.eswa.2018.07.057","article-title":"Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model","volume":"115","author":"Sefidian","year":"2019","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132446_b9015","article-title":"Tree-based Machine Learning Models for Predicting the Maximum Depth of Corrosion Defects Based on Historical In-line Inspection Data","volume":"100308","author":"Alshaye","year":"2025","journal-title":"Journal of Pipeline Science and Engineering"},{"key":"10.1016\/j.eswa.2026.132446_b0175","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1007\/s10479-022-04781-6","article-title":"Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm","volume":"345","author":"Sun","year":"2025","journal-title":"Annals of Operations Research"},{"key":"10.1016\/j.eswa.2026.132446_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.126812","article-title":"The asymmetric effects of oil price shocks on the world food prices: Fresh evidence from quantile-on-quantile regression approach","volume":"270","author":"Sun","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2026.132446_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2025.136604","article-title":"Study on influencing factors and forecast of global crude oil prices based on the hybrid model","volume":"328","author":"Wang","year":"2025","journal-title":"Energy"},{"key":"10.1016\/j.eswa.2026.132446_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109510","article-title":"Crude oil price forecasting with multivariate selection, machine learning, and a nonlinear combination strategy","volume":"139","author":"Xu","year":"2025","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132446_b0195","doi-asserted-by":"crossref","first-page":"7087","DOI":"10.1038\/s41598-025-90006-2","article-title":"Shapley value-driven superior subset selection algorithm for carbon price interval forecast combination","volume":"15","author":"Yang","year":"2025","journal-title":"Scientific Reports"},{"key":"10.1016\/j.eswa.2026.132446_b0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.cnsns.2025.108879","article-title":"Forecasting crude oil option prices with dynamic factors using integrated machine learning models","volume":"147","author":"Yu","year":"2025","journal-title":"Communications in Nonlinear Science and Numerical Simulation"},{"key":"10.1016\/j.eswa.2026.132446_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.127076","article-title":"Large Language Models Meet Energy Systems: Opportunities, challenges, and Future Perspectives","volume":"403","author":"Zhang","year":"2026","journal-title":"Applied Energy"},{"key":"10.1016\/j.eswa.2026.132446_b0210","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.eneco.2015.02.018","article-title":"A novel hybrid method for crude oil price forecasting","volume":"49","author":"Zhang","year":"2015","journal-title":"Energy Economics"},{"key":"10.1016\/j.eswa.2026.132446_b0215","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.eng.2024.06.017","article-title":"Petrochemical industry for the future","volume":"43","author":"Zhang","year":"2024","journal-title":"Engineering"},{"key":"10.1016\/j.eswa.2026.132446_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106273","article-title":"An improved temporal convolutional network with attention mechanism for photovoltaic generation forecasting","volume":"123","author":"Zhang","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.132446_b0225","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2025.106391","article-title":"A study on crude oil price forecasting model integrating CEEMDAN-VMD multiscale decomposition with CNN-BiLSTM","volume":"27","author":"Zhu","year":"2025","journal-title":"Results in Engineering"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095741742601359X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095741742601359X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:59:54Z","timestamp":1778781594000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095741742601359X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":45,"alternative-id":["S095741742601359X"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132446","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Bridging Discontinuities: A Large Language Model Augmented Uncertainty-Aware Framework for Short-Term Petrochemical Price Forecasting","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132446","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132446"}}