{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:23:34Z","timestamp":1768677814201,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities","award":["2021RCW116"],"award-info":[{"award-number":["2021RCW116"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["KJZD20191000401"],"award-info":[{"award-number":["KJZD20191000401"]}]},{"name":"the R&amp;D Program of Beijing Municipal Education Commission","award":["2021RCW116"],"award-info":[{"award-number":["2021RCW116"]}]},{"name":"the R&amp;D Program of Beijing Municipal Education Commission","award":["KJZD20191000401"],"award-info":[{"award-number":["KJZD20191000401"]}]},{"name":"Beijing Laboratory of National Economic Security Early-warning Engineering, Beijing Jiaotong University","award":["2021RCW116"],"award-info":[{"award-number":["2021RCW116"]}]},{"name":"Beijing Laboratory of National Economic Security Early-warning Engineering, Beijing Jiaotong University","award":["KJZD20191000401"],"award-info":[{"award-number":["KJZD20191000401"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The subject of oil price forecasting has obtained an incredible amount of interest from academics and policymakers in recent years due to the widespread impact that it has on various economic fields and markets. Thus, a novel method based on decomposition\u2013reconstruction\u2013ensemble for crude oil price forecasting is proposed. Based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique, in this paper we construct a recursive CEEMDAN decomposition\u2013reconstruction\u2013ensemble model considering the complexity traits of crude oil data. In this model, the steps of mode reconstruction, component prediction, and ensemble prediction are driven by complexity traits. For illustration and verification purposes, the West Texas Intermediate (WTI) and Brent crude oil spot prices are used as the sample data. The empirical result demonstrates that the proposed model has better prediction performance than the benchmark models. Thus, the proposed recursive CEEMDAN decomposition\u2013reconstruction\u2013ensemble model can be an effective tool to forecast oil price in the future.<\/jats:p>","DOI":"10.3390\/e25071051","type":"journal-article","created":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T01:43:22Z","timestamp":1689212602000},"page":"1051","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A New Forecasting Approach for Oil Price Using the Recursive Decomposition\u2013Reconstruction\u2013Ensemble Method with Complexity Traits"],"prefix":"10.3390","volume":"25","author":[{"given":"Fang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Laboratory of National Economic Security Early-Warning Engineering, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Menggang","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Laboratory of National Economic Security Early-Warning Engineering, Beijing Jiaotong University, Beijing 100044, China"},{"name":"National Academy of Economic Security, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Ruopeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Beijing Institute of Petrochemical Technology, Beijing 100044, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.neucom.2019.05.099","article-title":"Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition","volume":"361","author":"Buyuksahin","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.asoc.2019.02.006","article-title":"Deep belief network-based AR model for nonlinear time series forecasting","volume":"77","author":"Xu","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_3","unstructured":"Kantz, H., and Schreiber, T. (1997). Nonlinear Time Series Analysis: Contents, Cambridge University Press."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0169-2070(97)00044-7","article-title":"Forecasting with artificial neural networks: The state of the art","volume":"14","author":"Zhang","year":"1998","journal-title":"Int. J. Forecast."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1016\/j.asoc.2010.10.015","article-title":"A novel hybridization of artificial neural networks and ARIMA models for time series forecasting","volume":"11","author":"Khashei","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.neunet.2005.06.003","article-title":"A comparative study of autoregressive neural network hybrids","volume":"18","author":"Taskaya","year":"2005","journal-title":"Neural Netw."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.omega.2011.07.008","article-title":"Stock index forecasting based on a hybrid model","volume":"40","author":"Wang","year":"2012","journal-title":"Omega"},{"key":"ref_9","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":"ref_10","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1002\/for.2593","article-title":"Why do EMD-based methods improve prediction? A multiscale complexity perspective","volume":"38","author":"Dong","year":"2019","journal-title":"J. Forecast."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.apenergy.2015.07.025","article-title":"A decomposition\u2013ensemble model with datacharacteristic-driven reconstruction for crude oil price forecasting","volume":"156","author":"Yu","year":"2015","journal-title":"Appl. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107699","DOI":"10.1016\/j.asoc.2021.107699","article-title":"A memory-trait-driven decomposition\u2013reconstruction\u2013ensemble? learning paradigm for oil price forecasting","volume":"111","author":"Yu","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.ijpe.2018.05.019","article-title":"Another look at forecast selection and combination: Evidence from forecast pooling","volume":"209","author":"Kourentzes","year":"2019","journal-title":"Int. J. Prod. Econ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Guo, J. (2019, January 8\u201310). Oil price forecast using deep learning and ARIMA. Proceedings of the 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China.","DOI":"10.1109\/MLBDBI48998.2019.00054"},{"key":"ref_15","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 Econ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.22178\/pos.25-3","article-title":"Application of Markov model in crude oil price forecasting","volume":"3","author":"Isah","year":"2017","journal-title":"Traektoria Nauk."},{"key":"ref_17","unstructured":"Mirmirani, S., and Li, H. (2004). A Comparison of VAR and Neural Networks with Genetic Algorithm in Forecasting Price of oil Applications of Artificial Intelligence in Finance and Economics, Emerald Group Publishing Limited."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115149","DOI":"10.1016\/j.eswa.2021.115149","article-title":"A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction","volume":"182","author":"Zolfaghari","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"757","DOI":"10.3934\/jimo.2016045","article-title":"Hidden Markov models with threshold effects and their applications to oil price forecasting","volume":"13","author":"Zhu","year":"2017","journal-title":"J. Ind. Manag. Optim."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102244","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":"ref_21","doi-asserted-by":"crossref","first-page":"7893","DOI":"10.12973\/ejmste\/77926","article-title":"Assessing potentiality of support vector machine method in crude oil price forecasting","volume":"13","author":"Yu","year":"2017","journal-title":"EURASIA J. Math. Sci. Technol. Educ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.ijforecast.2018.03.009","article-title":"Crude oil price forecasting based on internet concern using an extreme learning machine","volume":"34","author":"Wang","year":"2018","journal-title":"Int. J. Forecast."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1016\/j.eneco.2011.07.018","article-title":"Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling","volume":"34","author":"Jammazi","year":"2012","journal-title":"Energy Econ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"120963","DOI":"10.1016\/j.energy.2021.120963","article-title":"A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting","volume":"231","author":"Urolagin","year":"2021","journal-title":"Energy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.eneco.2019.07.009","article-title":"Monthly crude oil spot price forecasting using variational mode decomposition","volume":"83","author":"Li","year":"2019","journal-title":"Energy Econ."},{"key":"ref_26","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":"ref_27","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.engappai.2015.04.016","article-title":"A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting","volume":"47","author":"Yu","year":"2016","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.eneco.2017.12.035","article-title":"The VEC-NAR model for short-term forecasting of oil prices","volume":"78","author":"Cheng","year":"2019","journal-title":"Energy Econ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.eneco.2021.105189","article-title":"Forecasting crude oil prices: A scaled PCA approach","volume":"97","author":"He","year":"2021","journal-title":"Energy Econ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, D.H., and Fang, T.H. (2022). Forecasting crude oil prices with a WT-FNN model. Energies, 15.","DOI":"10.3390\/en15061955"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106056","DOI":"10.1016\/j.eneco.2022.106056","article-title":"Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?","volume":"111","author":"Wang","year":"2022","journal-title":"Energy Econ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.ijforecast.2018.07.006","article-title":"Text-based crude oil price forecasting: A deep learning approach","volume":"35","author":"Li","year":"2019","journal-title":"Int. J. Forecast."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.energy.2018.04.133","article-title":"A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting","volume":"154","author":"Ding","year":"2018","journal-title":"Energy"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1080\/01605682.2022.2128908","article-title":"A novel hybrid method based on kernel-free support vector regression for stock indices and price forecasting","volume":"74","author":"Zheng","year":"2022","journal-title":"J. Oper. Res. Soc."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Torres, M.E., Colominas, M.A., Schlotthauer, G., and Flandrin, P. (2011, January 22\u201327). A complete ensemble empirical mode decomposition with adaptive noise. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"87","DOI":"10.2478\/v10117-011-0021-1","article-title":"Comparison of Values of Pearson\u2019s and Spearman\u2019s Correlation Coefficients on the Same Sets of Data","volume":"30","author":"Hauke","year":"2011","journal-title":"Quaest. Geogr."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.ijforecast.2019.08.014","article-title":"Predicting monthly biofuel production using a hybrid ensemble forecasting methodology","volume":"38","author":"Yu","year":"2019","journal-title":"Int. J. Forecast."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1016\/j.eneco.2007.02.012","article-title":"A new approach for crude oil price analysis based on Empirical Mode Decomposition","volume":"30","author":"Zhang","year":"2008","journal-title":"Energy Econ."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/7\/1051\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:11:32Z","timestamp":1760127092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/25\/7\/1051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,12]]},"references-count":39,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["e25071051"],"URL":"https:\/\/doi.org\/10.3390\/e25071051","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,12]]}}}