{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:08:43Z","timestamp":1784218123398,"version":"3.55.0"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Guangdong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2023B1515120020"],"award-info":[{"award-number":["2023B1515120020"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Guangdong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2024A1515012040"],"award-info":[{"award-number":["2024A1515012040"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Guangdong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2026A1515011166"],"award-info":[{"award-number":["2026A1515011166"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006056"],"award-info":[{"award-number":["62006056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.asoc.2026.115618","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T05:49:41Z","timestamp":1780292981000},"page":"115618","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PB","title":["A synergistic multi-mechanism deep learning model for gasoline futures price forecasting"],"prefix":"10.1016","volume":"201","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6558-3443","authenticated-orcid":false,"given":"Fei","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8207-0496","authenticated-orcid":false,"given":"Yu-Feng","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying-Chao","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulin","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.115618_bib1","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.eswa.2016.08.045","article-title":"Forecasting volatility of oil price using an artificial neural network-GARCH model","volume":"65","author":"Kristjanpoller","year":"2016","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115618_bib2","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.jempfin.2003.07.002","article-title":"Chasing trends: recursive moving average trading rules and internet stocks","volume":"12","author":"Fong","year":"2005","journal-title":"J. Empir. Financ."},{"key":"10.1016\/j.asoc.2026.115618_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.resourpol.2021.102244","article-title":"Forecasting crude oil real prices with averaging time-varying VAR models","volume":"74","author":"Drachal","year":"2021","journal-title":"Resour. Policy"},{"key":"10.1016\/j.asoc.2026.115618_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.resourpol.2022.102570","article-title":"Forecasting crude oil market returns: enhanced moving average technical indicators","volume":"76","author":"Wen","year":"2022","journal-title":"Resour. Policy"},{"key":"10.1016\/j.asoc.2026.115618_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.eneco.2023.107089","article-title":"Forecasting crude oil futures price using machine learning methods: evidence from China","volume":"127","author":"Guo","year":"2023","journal-title":"Energy Econ."},{"key":"10.1016\/j.asoc.2026.115618_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122891","article-title":"An enhanced interval-valued decomposition integration model for stock price prediction based on comprehensive feature extraction and optimized deep learning","volume":"243","author":"Wang","year":"2024","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115618_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124962","article-title":"Wavelet-Based precipitation preprocessing for improved drought Forecasting: a machine learning approach using tunable Q-factor wavelet transform and maximal overlap discrete wavelet transform","volume":"257","author":"Osmani","year":"2024","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115618_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2025.122328","article-title":"Robustness and orthogonality: time series forecasting via wavelets","volume":"717","author":"Tian","year":"2025","journal-title":"Inform. Sci."},{"key":"10.1016\/j.asoc.2026.115618_bib9","article-title":"Dual decomposition-enhanced integrated deep networks with bidirectional CNN and semi-supervised GRU for multivariate nonlinear time series forecasting","volume":"735","author":"Zhao","year":"2025","journal-title":"Inform. Sci."},{"key":"10.1016\/j.asoc.2026.115618_bib10","first-page":"227","article-title":"A novel hybrid GA based SVM short term load forecasting model","volume":"Nov. 2009","author":"Sun","year":"2009"},{"key":"10.1016\/j.asoc.2026.115618_bib11","series-title":"The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting","author":"Werbos","year":"1994"},{"key":"10.1016\/j.asoc.2026.115618_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121968","article-title":"Improving prediction efficiency of Chinese stock index futures intraday price by VIX-Lasso-GRU Model","volume":"238","author":"Fang","year":"2024","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115618_bib13","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/S0893-6080(98)00018-5","article-title":"How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies","volume":"11","author":"Lin","year":"1998","journal-title":"Neural Netw."},{"key":"10.1016\/j.asoc.2026.115618_bib14","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.energy.2016.02.098","article-title":"Forecasting energy market indices with recurrent neural networks: case study of crude oil price fluctuations","volume":"102","author":"Wang","year":"2016","journal-title":"Energy"},{"key":"10.1016\/j.asoc.2026.115618_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.112842","article-title":"Improving DWT-RNN model via B-spline wavelet multiresolution to forecast a high-frequency time series","volume":"138","author":"Hajiabotorabi","year":"2019","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115618_bib16","doi-asserted-by":"crossref","DOI":"10.1016\/j.najef.2021.101421","article-title":"Forecasting stock index price using the CEEMDAN-LSTM model","volume":"57","author":"Lin","year":"2021","journal-title":"N. Am. J. Econ. Financ."},{"key":"10.1016\/j.asoc.2026.115618_bib17","doi-asserted-by":"crossref","first-page":"44640","DOI":"10.1109\/ACCESS.2024.3380480","article-title":"The analysis of deep learning recurrent neural network in English grading under the internet of things","volume":"12","author":"Li","year":"2024","journal-title":"IEEE Access."},{"key":"10.1016\/j.asoc.2026.115618_bib18","doi-asserted-by":"crossref","first-page":"2718","DOI":"10.3390\/electronics12122718","article-title":"Improved BIGRU model and its application in stock price forecasting","volume":"12","author":"Duan","year":"2023","journal-title":"Electronics"},{"key":"10.1016\/j.asoc.2026.115618_bib19","doi-asserted-by":"crossref","first-page":"3135","DOI":"10.1080\/15435075.2024.2356103","article-title":"Improving short-term forecasting of solar power generation by using an EEMD-BiGRU model: a comparative study based on seven standalone models and six hybrid models","volume":"21","author":"Jia","year":"2024","journal-title":"Int. J. Green Energy"},{"key":"10.1016\/j.asoc.2026.115618_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113971","article-title":"mLANet: an efficient recurrent neural network for long-term time series forecasting","volume":"325","author":"Jiang","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.asoc.2026.115618_bib21","doi-asserted-by":"crossref","DOI":"10.1142\/S1469026823500219","article-title":"Text classification based on CNN-BiGRU and its application in telephone comments recognition","volume":"22","author":"Wang","year":"2023","journal-title":"Int. J. Comput. Intell. Appl."},{"key":"10.1016\/j.asoc.2026.115618_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121899","article-title":"Attention based adaptive spatial\u2013temporal hypergraph convolutional networks for stock price trend prediction","volume":"238","author":"Su","year":"2024","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115618_bib23","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s12145-024-01524-y","article-title":"A singular spectrum analysis-enhanced BiTCN-selfattention model for runoff prediction","volume":"18","author":"Wang","year":"2024","journal-title":"Earth Sci. Inform."},{"key":"10.1016\/j.asoc.2026.115618_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.114284","article-title":"A long- and short-term feature fusion network for wind speed forecasting","volume":"187","author":"Geng","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115618_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2026.114635","article-title":"PMNet: a progressive MLP-based framework for time series forecasting with long-short term dependency synergy","volume":"190","author":"Chen","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115618_bib26","first-page":"240","article-title":"Improved ant colony optimization routing algorithm for UAV ad-hoc network based on Link Quality Prediction","volume":"23","author":"Zeng","year":"2025","journal-title":"J. Terahertz Sci. Electron. Inf. Technol."},{"key":"10.1016\/j.asoc.2026.115618_bib27","first-page":"422","article-title":"Prediction of output power of photovoltaic power stations based on BiTCN-BiGRU-AM","author":"Yuan","year":"2025","journal-title":"Shanghai Energy Conserv."},{"key":"10.1016\/j.asoc.2026.115618_bib28","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109723","article-title":"Forecasting crude oil futures prices using BiLSTM-Attention-CNN model with Wavelet transform","volume":"130","author":"Lin","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115618_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119617","article-title":"Oil price forecasting: a hybrid GRU neural network based on decomposition\u2013reconstruction methods","volume":"218","author":"Zhang","year":"2023","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115618_bib30","unstructured":"Jin, M. et al., Time-LLM: Time Series Forecasting by Reprogramming Large Language Models, arXiv preprint arXiv:2310.01728 (stat.ML), 2023."},{"key":"10.1016\/j.asoc.2026.115618_bib31","article-title":"EnergyFormer: dual-supervised adaptive multi-scale transformer for multistep energy forecasting","volume":"305","author":"Shao","year":"2025","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115618_bib32","article-title":"AFMT: adaptive frequency decomposition and multi-scale transformer for time series forecasting","volume":"726","author":"Zhu","year":"2025","journal-title":"Inform. Sci."},{"key":"10.1016\/j.asoc.2026.115618_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113846","article-title":"CAWformer: a cross variable attention with discrete wavelet denoising for multivariate time series forecasting","volume":"324","author":"Fan","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.asoc.2026.115618_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2026.114979","article-title":"TiVaT: a transformer with a single unified mechanism for capturing asynchronous dependencies in multivariate time series forecasting","author":"Ha","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115618_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114088","article-title":"PerioDformer: periodic disposition enhanced transformer for times series forecasting","volume":"327","author":"Tang","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.asoc.2026.115618_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113551","article-title":"MSTVI: multi-scale time-variable interaction for multivariate time series forecasting","volume":"319","author":"Liu","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.asoc.2026.115618_bib37","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1016\/j.eneco.2008.05.003","article-title":"Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm","volume":"30","author":"Yu","year":"2008","journal-title":"Energy Econ."},{"key":"10.1016\/j.asoc.2026.115618_bib38","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.asoc.2026.115618_bib39","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1177\/1077546317707103","article-title":"Laplace wavelet transform theory and applications","volume":"24","author":"Abuhamdia","year":"2018","journal-title":"J. Vib. Control."},{"key":"10.1016\/j.asoc.2026.115618_bib40","first-page":"207","article-title":"Continuous wavelet transforms","volume":"1","author":"Shi","year":"2004"},{"key":"10.1016\/j.asoc.2026.115618_bib41","first-page":"1220","article-title":"On the initialization of the discrete wavelet transform algorithm","volume":"2","author":"Liu","year":"1997"},{"key":"10.1016\/j.asoc.2026.115618_bib42","first-page":"401","article-title":"Wavelet transforms with discrete-time continuous dilation wavelets","author":"Zhao","year":"1999","journal-title":"Conf. Wavel. Appl."},{"key":"10.1016\/j.asoc.2026.115618_bib43","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: the wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115618_bib44","series-title":"Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR \u201918)","first-page":"163","article-title":"Entire information attentive GRU for text representation","author":"He","year":"2018"},{"key":"10.1016\/j.asoc.2026.115618_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2021.121082","article-title":"Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network","volume":"233","author":"Wang","year":"2021","journal-title":"Energy"},{"issue":"1","key":"10.1016\/j.asoc.2026.115618_bib46","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1007\/s10291-024-01809-1","article-title":"Clock bias prediction of navigation satellite based on BWO-CNN-BiGRU-attention model","volume":"29","author":"Sun","year":"2024","journal-title":"GPS Solut."},{"key":"10.1016\/j.asoc.2026.115618_bib47","first-page":"250","article-title":"BiGRU-CNN-AT: classifiying emotion on social media","volume":"59","author":"Amriza","year":"2025","journal-title":"Data Technol. Appl."},{"key":"10.1016\/j.asoc.2026.115618_bib48","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1016\/j.egyr.2023.05.121","article-title":"RUL prediction of lithium-ion batteries based on CEEMDAN-CNN BiLSTM model","volume":"9","author":"Guo","year":"2023","journal-title":"Energy Rep."},{"key":"10.1016\/j.asoc.2026.115618_bib49","series-title":"2021 3rd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)","first-page":"40","article-title":"Film review sentiment classification based on BiGRU and attention","author":"Yang","year":"2021"},{"key":"10.1016\/j.asoc.2026.115618_bib50","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11106","article-title":"Informer: beyond efficient transformer for long sequence time-series forecasting","author":"Zhou","year":"2021"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626010665?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626010665?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:55:02Z","timestamp":1784217302000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626010665"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":50,"alternative-id":["S1568494626010665"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115618","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A synergistic multi-mechanism deep learning model for gasoline futures price forecasting","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115618","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115618"}}