{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T02:16:54Z","timestamp":1769048214941,"version":"3.49.0"},"reference-count":35,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T00:00:00Z","timestamp":1592524800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["GS"],"published-print":{"date-parts":[[2020,6,19]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>According to the problem of crude oil price forecasting, the purpose of this paper is to propose a multi-step prediction method based on the empirical mode decomposition, long short-term memory network and GM (1,1) model.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>First, the empirical mode decomposition method is used to decompose the crude oil price series into several components with different frequencies. Then, each subsequence is classified and synthesized based on the specific periodicity and other properties to obtain several components with different significant characteristics. Finally, all components are substituted into a suitable prediction model for fitting. LSTM models with different parameters are constructed for predicting specific components, which approximately and respectively represent short-term market disturbance and long-term influences. Rolling GM (1,1) model is constructed to simulate a series representing the development trend of oil price. Eventually, all results obtained from forecasting models are summarized to evaluate the performance of the model.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The model is respectively applied to simulate daily, weekly and monthly WTI crude oil price sequences. The results show that the model has high accuracy on the prediction, especially in terms of series representing long-term influences with lower frequency. GM (1,1) model has excellent performance on fitting the trend of crude oil price.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This paper combines GM (1,1) model with LSTM network to forecast WTI crude oil price series. According to the different characteristics of different sequences, suitable forecasting models are constructed to simulate the components.<\/jats:p><\/jats:sec>","DOI":"10.1108\/gs-03-2020-0031","type":"journal-article","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T10:16:44Z","timestamp":1592561804000},"page":"80-94","source":"Crossref","is-referenced-by-count":24,"title":["Crude oil price prediction based on LSTM network and GM (1,1) model"],"prefix":"10.1108","volume":"11","author":[{"given":"Tianxiang","family":"Yao","sequence":"first","affiliation":[]},{"given":"Zihan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2021021515241548600_ref001","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1016\/j.physa.2017.11.032","article-title":"Analysis of the impact of crude oil price fluctuations on China's stock market in different periods\u2014based on time series network model","volume":"492","year":"2018","journal-title":"Physica A: Statistical Mechanics and Its Applications"},{"key":"key2021021515241548600_ref002","doi-asserted-by":"crossref","first-page":"101017","DOI":"10.1016\/j.ecoinf.2019.101017","article-title":"Image-based species identification of wild bees using convolutional neural networks","volume":"55","year":"2020","journal-title":"Ecological Informatics"},{"key":"key2021021515241548600_ref003","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.energy.2018.12.016","article-title":"Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer","volume":"169","year":"2019","journal-title":"Energy"},{"key":"key2021021515241548600_ref004","doi-asserted-by":"crossref","first-page":"6152","DOI":"10.1016\/j.amc.2012.12.015","article-title":"The necessary and sufficient condition for GM (1,1) grey prediction mode","volume":"219","year":"2013","journal-title":"Applied Mathematics and Computation"},{"key":"key2021021515241548600_ref005","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.eneco.2016.04.018","article-title":"Impacts of OPEC's political risk on the international crude oil prices: an empirical analysis based on the SVAR models","volume":"57","year":"2016","journal-title":"Energy Economics"},{"key":"key2021021515241548600_ref006","first-page":"97","article-title":"On the effectiveness of GM (1,1) model with different sequence sizes: an evidence from random simulations","volume":"30","year":"2018","journal-title":"Journal of Grey System"},{"key":"key2021021515241548600_ref007","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.physa.2018.02.061","article-title":"Muli-step-ahead crude oil price forecasting using a hybird grey wave model","volume":"501","year":"2018","journal-title":"Physic A: Statistical Mechanics and its Applications"},{"key":"key2021021515241548600_ref008","first-page":"1","article-title":"Three properties of grey forecasting model GM (1,1)- the issue on the optimization structure and optimization information volume of grey predictive control","volume":"5","year":"1987","journal-title":"HuaZhong University Science and Technology"},{"key":"key2021021515241548600_ref009","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","year":"1997","journal-title":"Neural Computation"},{"key":"key2021021515241548600_ref010","doi-asserted-by":"crossref","first-page":"103032","DOI":"10.1016\/j.infrared.2019.103032","article-title":"LSTM-RNN-based defect classification in honeycomb structures using infrared thermography","volume":"102","year":"2019","journal-title":"Infrared Physics and Technology"},{"key":"key2021021515241548600_ref011","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.econmod.2018.12.006","article-title":"Do shale gas and oil productions move in convergence? 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