1. Quantile Analysis of Oil Price Shocks and Stock Market Performance: A European Perspective. (2025). Audi, Marc ; Ali, Amjad ; Poulin, Marc ; Ahmad, Khalil.
   In: MPRA Paper.
   RePEc:pra:mprapa:124295.

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2. Revealing spatiotemporal connections in container hub ports under adverse events through link prediction. (2025). Bo-Wei, XU ; Jun-Jun, LI ; Yu-Tao, Tian.
   In: Journal of Transport Geography.
   RePEc:eee:jotrge:v:125:y:2025:i:c:s0966692325000894.

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3. A novel forecasting framework leveraging large language model and machine learning for methanol price. (2025). Sui, Cong ; Wang, Jinglin ; Ma, Mingrui ; Luo, Yuping.
   In: Energy.
   RePEc:eee:energy:v:320:y:2025:i:c:s0360544225007650.

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1. Abdi, H. ; Williams, L.J. Principal component analysis. 2010 Wiley Interdiscip Rev: Computat Statist. 2 433-459
   Paper not yet in RePEc: Add citation now
   

2. Abdollahi, H. ; Ebrahimi, S. A new hybrid model for forecasting Brent crude oil price. 2020 Energy. 200 -

3. Anghinoni, L. ; Zhao, L. ; Ji, D. Time series trend detection and forecasting using complex network topology analysis. 2019 Neural Netw. 117 295-306
   Paper not yet in RePEc: Add citation now
   

4. Azevedo, V. ; Campos, L. Combination of forecasts for the price of crude oil on the spot market. 2016 Int J Prod Res. 54 5219-5235

5. Bai, Y. ; Li, X. ; Yu, H. ; Jia, S. Crude oil price forecasting incorporating news text. 2022 Int J Forecast. 38 367-383

6. Baumeister, C. ; Kilian, L. Forty years of oil price fluctuations: why the price of oil may still surprise us. 2016 J Econ Perspect. 30 139-160

7. Berahmand, K. ; Nasiri, E. ; Forouzandeh, S. A preference random walk algorithm for link prediction through mutual influence nodes in complex networks. 2022 J King Saud Univ-Comput inform Sci. 34 5375-5387
   Paper not yet in RePEc: Add citation now
   
8. Blei, D. ; Ng, A. ; Jordan, M. Latent dirichlet allocation. 2003 J Mach Learn Res. 3 993-1022
   Paper not yet in RePEc: Add citation now
   
9. Boyd-Graber, J. ; Hu, Y. ; Mimno, D. Applications of topic models. Foundations and trends®. 2017 Inf Retr. 11 143-296
   Paper not yet in RePEc: Add citation now
   

10. Charfeddine, L. Breaks or long range dependence in the energy futures volatility: out-of-sample forecasting and VaR analysis. 2016 Econ Model. 53 354-374

11. Chiroma, H. ; Abdulkareem, S. ; Herawan, T. Evolutionary neural network model for West Texas intermediate crude oil price prediction. 2015 Appl Energy. 142 266-273

12. Diebold, F. ; Mariano, R. Comparing predictive accuracy. 2002 J Bus Econ Stat. 20 134-144

13. Gao, Q. ; Wen, T. ; Deng, Y. A novel network-based and divergence-based time series forecasting method. 2022 Inf Sci. 612 553-562
    Paper not yet in RePEc: Add citation now
    

14. Ginsberg, J. ; Mohebbi, M. ; Patel, R. ; Brammer, L. ; Smolinski, M. ; Brilliant, L. Detecting influenza epidemics using search engine query data. 2009 Nature. 457 1012-1014

15. Güne, S.I. ; Gündüz-Ö güdücü, S. ; Çataltepe, Z. Link prediction using time series of neighborhood-based node similarity scores. 2016 Data Min Knowl Disc. 30 147-180
    Paper not yet in RePEc: Add citation now
    

16. He, Z. Dynamic impacts of crude oil price on Chinese investor sentiment: nonlinear causality and time-varying effect. 2020 Int Rev Econ Financ. 66 131-153

17. Hu, Y. ; Chu, C. ; Wu, P. ; Hu, J. A linear time series analysis of carbon price via a complex network approach. 2022 Front Phys. 10 -
    Paper not yet in RePEc: Add citation now
    
18. Ji, P. ; Ye, J. ; Mu, Y. ; Lin, W. ; Tian, Y. ; Hens, C. Signal propagation in complex networks. 2023 Phys Rep. 1017 1-96
    Paper not yet in RePEc: Add citation now
    
19. Jiang, M. ; Chen, W. ; Xu, H. A novel interval dual convolutional neural network method for interval-valued stock price prediction. 2024 Pattern Recogn. 145 -
    Paper not yet in RePEc: Add citation now
    
20. Lacasa, L. ; Luque, B. ; Ballesteros, F. ; Luque, J. ; Nuno, J. From time series to complex networks: the visibility graph. 2008 Proc Natl Acad Sci. 105 4972-4975
    Paper not yet in RePEc: Add citation now
    
21. Li, J. ; Hong, Z. ; Zhang, C. ; Wu, J. ; Yu, C. A novel hybrid model for crude oil price forecasting based on MEEMD and mix-KELM. 2024 Expert Syst Appl. 246 -
    Paper not yet in RePEc: Add citation now
    

22. Li, J. ; Qian, S. ; Li, L. ; Guo, Y. ; Wu, J. ; Tang, L. A novel secondary decomposition method for forecasting crude oil price with twitter sentiment. 2024 Energy. 290 -

23. Li, L. ; Wen, S. ; Tseng, M. ; Chiu, A. Photovoltaic array prediction on short-term output power method in centralized power generation system. 2020 Ann Oper Res. 290 243-263

24. Li, X. ; Li, S.W. Nonlinear time series forecasting model based on EMD and DVG and its application. 2022 Chin J Manage Sci. 30 275-286
    Paper not yet in RePEc: Add citation now
    

25. Li, X. ; Shang, W. ; Wang, S. Text-based crude oil price forecasting: a deep learning approach. 2019 Int J Forecast. 35 1548-1560

26. Liang, X. ; Wang, Y. ; Yang, M. Systemic modeling and prediction of port container throughput using hybrid link analysis in complex networks. 2024 Systems. 12 23-
    Paper not yet in RePEc: Add citation now
    

27. Liu, T. ; Qi, S. ; Qiao, X. A hybrid short-term wind power point-interval prediction model based on combination of improved preprocessing methods and entropy weighted GRU quantile regression network. 2024 Energy. 288 -

28. Liu, W. ; Lü, L. Link prediction based on local random walk. 2010 Europhys Lett. 89 58007-
    Paper not yet in RePEc: Add citation now
    

29. Luo, R. ; Liu, J. ; Wang, P. ; Tao, Z. ; Chen, H. A multisource data-driven combined forecasting model based on internet search keyword screening method for interval soybean futures price. 2024 J Forecast. -

30. Ma, Z. ; Yang, X. ; He, A. Complex network recognition of electrocardiograph signals in health and myocardial infarction patients based on multiplex visibility graph. 2022 Acta Phys Sin. 71 48-57
    Paper not yet in RePEc: Add citation now
    
31. Maia, A. ; de Carvalho, F. Holt’s exponential smoothing and neural network models for forecasting interval-valued time series. 2011 Int J Forecast. 27 740-759
    Paper not yet in RePEc: Add citation now
    
32. Mao, S. ; Zeng, X. SimVGNets: similarity-based visibility graph networks for carbon price forecasting. 2023 Expert Syst Appl. 230 -
    Paper not yet in RePEc: Add citation now
    

33. Mei, D. ; Ma, F. ; Liao, Y. ; Wang, L. Geopolitical risk uncertainty and oil future volatility: evidence from MIDAS models. 2020 Energy Econ. 86 -

34. Newman, M. Networks: An introduction. 2010 Oxford University Press:
    Paper not yet in RePEc: Add citation now
    
35. Quan, H. ; Zhang, W. ; Zhang, W. An interval prediction approach of wind power based on skip-GRU and block-bootstrap techniques. 2023 IEEE Trans Ind Appl. 59 4710-4719
    Paper not yet in RePEc: Add citation now
    
36. Ravi, K. ; Ravi, V. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. 2015 Knowl-Based Syst. 89 14-46
    Paper not yet in RePEc: Add citation now
    
37. Ren, W. ; Jin, N. ; Lei o., Phase space graph convolutional network for chaotic time series learning. 2024 IEEE Trans Industr Inform. -
    Paper not yet in RePEc: Add citation now
    

38. Sen, A. ; Choudhury, K. Forecasting the crude oil prices for last four decades using deep learning approach. 2024 Res Policy. 88 -

39. Sun, B. ; Bai, J. ; Chu, X. Interval prediction approach to crude oil price based on three-way clustering and decomposition ensemble learning. 2022 Appl Soft Comput. 123 -
    Paper not yet in RePEc: Add citation now
    

40. Sun, J. ; Zhao, P. ; Sun, S. A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting. 2022 Res Policy. 77 -

41. Sun, S. ; Sun, Y. ; Wang, S. Interval decomposition ensemble approach for crude oil price forecasting. 2018 Energy Econ. 76 274-287

42. Syuhada, K. ; Tjahjono, V. ; Hakim, A. Improving value-at-risk forecast using GA-ARMA-GARCH and AI-KDE models. 2023 Appl Soft Comput. 148 -
    Paper not yet in RePEc: Add citation now
    

43. Tang, L. ; Zhang, C. ; Li, L. ; Wang, S. A multi-scale method for forecasting oil price with multi-factor search engine data. 2020 Appl Energy. 257 -

44. Wang, J. ; Wang, J. ; Zhang, Z. ; Guo, S. Forecasting stock indices with back propagation neural network. 2011 Expert Syst Appl. 38 14346-14355
    Paper not yet in RePEc: Add citation now
    
45. Wang, J. ; Zhou, Y. ; Jiang, H. A novel interval forecasting system based on multi-objective optimization and hybrid data reconstruct strategy. 2023 Expert Syst Appl. 217 -
    Paper not yet in RePEc: Add citation now
    

46. Wang, K. ; Wang, J. ; Zeng, B. An integrated power load point-interval forecasting system based on information entropy and multi-objective optimization. 2022 Appl Energy. 314 -

47. Wang, M. ; Tian, L. ; Zhou, P. A novel approach for oil price forecasting based on data fluctuation network. 2018 Energy Econ. 71 201-212

48. Wang, P. ; Tao, Z. ; Liu, J. Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode. 2023 Energy Econ. 118 -

49. Wang, X. ; Li, X. ; Li, S. Point and interval forecasting system for crude oil price based on complete ensemble extreme-point symmetric mode decomposition with adaptive noise and intelligent optimization algorithm. 2022 Appl Energy. 328 -

50. Wong, K. ; Song, H. ; Witt, S. Tourism forecasting: to combine or not to combine?. 2007 Tour Manag. 28 1068-1078
    Paper not yet in RePEc: Add citation now
    
51. Wu, B. ; Wang, L. ; Lv, S. ; Zeng, Y. Effective crude oil price forecasting using new text-based and big-data-driven model. 2021 Measurement. 168 -
    Paper not yet in RePEc: Add citation now
    

52. Wu, B. ; Wang, L. ; Wang, S. ; Zeng, Y. Forecasting the US oil markets based on social media information during the COVID-19 pandemic. 2021 Energy. 226 -

53. Xiong, T. ; Bao, Y. ; Hu, Z. ; Chiong, R. Forecasting interval time series using a fully complex-valued RBF neural network with DPSO and PSO algorithms. 2015 Inf Sci. 305 77-92
    Paper not yet in RePEc: Add citation now
    

54. Xu, H. ; Wang, M. ; Jiang, S. ; Yang, W. Carbon price forecasting with complex network and extreme learning machine. 2020 Phys A: Statist Mech Appl. 545 -

55. Yang, K. ; Cheng, Z. ; Li, M. ; Wang, S. ; Wei, Y. Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy. 2024 Appl Energy. 353 -

56. Yang, W. ; Han, A. ; Hong, Y. ; Wang, S. Analysis of crisis impact on crude oil prices: a new approach with interval time series modeling. 2016 Quant Financ. 16 1917-1928

57. Yang, Y. ; Guo, J. ; Sun, S. ; Li, Y. Forecasting crude oil price with a new hybrid approach and multi-source data. 2021 Eng Appl Artif Intell. 101 -
    Paper not yet in RePEc: Add citation now
    

58. Yin, X. ; Yang, X. ; Yang, Z. Using the R/S method to determine the periodicity of time series. 2009 Chaos, Solitons Fractals. 39 731-745

59. Yu, C. ; Zhao, X. ; An, L. Similarity-based link prediction in social networks: a path and node combined approach. 2017 J Inf Sci. 43 683-695
    Paper not yet in RePEc: Add citation now
    

60. Zhang, L. ; Wang, J. ; Wang, B. Energy market prediction with novel long short-term memory network: case study of energy futures index volatility. 2020 Energy. 211 -

61. Zheng, L. ; Sun, Y. ; Wang, S. A novel interval-based hybrid framework for crude oil price forecasting and trading. 2024 Energy Econ. 130 -

1. Machine learning price index forecasts of flat steel products. (2025). Xu, Xiaojie ; Jin, Bingzi.
   In: Mineral Economics.
   RePEc:spr:minecn:v:38:y:2025:i:1:d:10.1007_s13563-024-00457-8.

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2. Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm. (2025). Chen, Heli ; Wang, Yong ; Sun, Weixin ; Liu, Feng.
   In: Annals of Operations Research.
   RePEc:spr:annopr:v:345:y:2025:i:2:d:10.1007_s10479-022-04781-6.

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3. Forecasting crude oil prices: A Gated Recurrent Unit-based nonlinear Granger Causality model. (2025). Zhang, Dayong ; Lu, Quanying ; Guo, Mengzhuo ; Lin, Qingyuan ; Liang, Qian.
   In: International Review of Financial Analysis.
   RePEc:eee:finana:v:102:y:2025:i:c:s105752192500211x.

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4. Data-driven machine learning models for predicting deliverability of underground natural gas storage in aquifer and depleted reservoirs. (2025). Zheng, Qingsong ; Feng, Yujie ; Hussain, Javid ; Pan, Peng-Zhi.
   In: Energy.
   RePEc:eee:energy:v:319:y:2025:i:c:s0360544225006164.

   Full description at Econpapers || Download paper

5. Artificial Intelligence for Water Consumption Assessment: State of the Art Review. (2024). Anandhi, Aavudai ; Delhom, Christopher ; Ilangovan, Nivedita ; Morain, Almando.
   In: Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA).
   RePEc:spr:waterr:v:38:y:2024:i:9:d:10.1007_s11269-024-03823-x.

   Full description at Econpapers || Download paper

6. Effective Crude Oil Prediction Using CHS-EMD Decomposition and PS-RNN Model. (2024). Patil, Renuka ; Swasthika, T J ; Chaithanya, B N ; Chellin, George J ; Ruby, Usha A.
   In: Computational Economics.
   RePEc:kap:compec:v:64:y:2024:i:2:d:10.1007_s10614-023-10460-w.

   Full description at Econpapers || Download paper

7. Resilience through mineral resource development, oil, and natural resource efficiency: Strengthening economies. (2024). Nazir, Sidra ; Yan, Youliang ; Jia, Miaoyin ; Lu, Gan.
   In: Resources Policy.
   RePEc:eee:jrpoli:v:91:y:2024:i:c:s030142072400309x.

   Full description at Econpapers || Download paper

8. Oil market regulatory: An ensembled model for prediction. (2024). Liu, Yancheng ; Gu, Xiang ; Fan, Kun ; Chen, Haixin.
   In: Finance Research Letters.
   RePEc:eee:finlet:v:67:y:2024:i:pa:s1544612324008195.

   Full description at Econpapers || Download paper

9. A novel link prediction model for interval-valued crude oil prices based on complex network and multi-source information. (2024). Tao, Zhifu ; Luo, Rui ; Zhao, Xiaoman ; Liu, Jinpei.
   In: Applied Energy.
   RePEc:eee:appene:v:376:y:2024:i:pb:s0306261924016441.

   Full description at Econpapers || Download paper

10. Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products. (2023). Zhang, Yun ; Xu, Xiaojie.
    In: Mineral Economics.
    RePEc:spr:minecn:v:36:y:2023:i:4:d:10.1007_s13563-022-00357-9.

    Full description at Econpapers || Download paper

11. Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models. (2023). Rodrigues, Paulo Canas ; Turpo-Chaparro, Josue E ; Lopez-Gonzales, Javier Linkolk ; Iftikhar, Hasnain ; Zafar, Aimel.
    In: Mathematics.
    RePEc:gam:jmathe:v:11:y:2023:i:16:p:3548-:d:1218691.

    Full description at Econpapers || Download paper

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    In: Resources Policy.
    RePEc:eee:jrpoli:v:85:y:2023:i:pb:s0301420723006670.

    Full description at Econpapers || Download paper

13. Green finance and the socio-politico-economic factors’ impact on the future oil prices: Evidence from machine learning. (2023). Mohsin, Muhammad ; Jamaani, Fouad.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:85:y:2023:i:pa:s0301420723004919.

    Full description at Econpapers || Download paper

14. Forecasting the crude oil prices with an EMD-ISBM-FNN model. (2023). Fang, Tianhui ; Wang, Donghua ; Zheng, Chunling.
    In: Energy.
    RePEc:eee:energy:v:263:y:2023:i:pa:s0360544222022897.

    Full description at Econpapers || Download paper

15. Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector. (2023). Wang, Z ; Tang, Y ; Tontiwachwuthikul, P ; Javanmard, Emami M.
    In: Applied Energy.
    RePEc:eee:appene:v:338:y:2023:i:c:s0306261923001940.

    Full description at Econpapers || Download paper

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    Full description at Econpapers || Download paper

17. A Novel βSA Ensemble Model for Forecasting the Number of Confirmed COVID-19 Cases in the US. (2022). Wu, Ting-Wei ; Shih, Ming-Hung ; Yang, Min-Jui ; Yen, David C.
    In: Mathematics.
    RePEc:gam:jmathe:v:10:y:2022:i:5:p:824-:d:764172.

    Full description at Econpapers || Download paper

18. A Mutual Information-Based Network Autoregressive Model for Crude Oil Price Forecasting Using Open-High-Low-Close Prices. (2022). Tabriz, Yousef Edrisi ; Shadabfar, Mahboubeh ; Khoojine, Arash Sioofy.
    In: Mathematics.
    RePEc:gam:jmathe:v:10:y:2022:i:17:p:3172-:d:905728.

    Full description at Econpapers || Download paper

19. Series Hybridization of Parallel (SHOP) models for time series forecasting. (2022). Hajirahimi, Zahra ; Khashei, Mehdi.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:596:y:2022:i:c:s0378437122001777.

    Full description at Econpapers || Download paper

20. Predicting the changes in the WTI crude oil price dynamics using machine learning models. (2022). Guliyev, Hasraddin ; Mustafayev, Eldayag.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:77:y:2022:i:c:s030142072200112x.

    Full description at Econpapers || Download paper

21. Forecasting crude oil market returns: Enhanced moving average technical indicators. (2022). Zhang, Yaojie ; Wang, Yudong ; Liu, LI ; Wen, Danyan.
    In: Resources Policy.
    RePEc:eee:jrpoli:v:76:y:2022:i:c:s0301420722000216.

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22. A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features. (2022). Li, Xiuming ; Sun, Mei ; Mensah, Isaac Adjei ; He, Huizi.
    In: Energy.
    RePEc:eee:energy:v:244:y:2022:i:pa:s0360544221029558.

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23. Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization. (2022). Karasu, Sekin ; Altan, Ayta.
    In: Energy.
    RePEc:eee:energy:v:242:y:2022:i:c:s0360544221032138.

    Full description at Econpapers || Download paper

24. A novel class of reliability-based parallel hybridization (RPH) models for time series forecasting. (2022). Hajirahimi, Zahra ; Etemadi, Sepideh ; Khashei, Mehdi.
    In: Chaos, Solitons & Fractals.
    RePEc:eee:chsofr:v:156:y:2022:i:c:s0960077922000911.

    Full description at Econpapers || Download paper

25. Point and interval forecasting system for crude oil price based on complete ensemble extreme-point symmetric mode decomposition with adaptive noise and intelligent optimization algorithm. (2022). Wang, Xuerui ; Li, Xiangyu.
    In: Applied Energy.
    RePEc:eee:appene:v:328:y:2022:i:c:s0306261922014519.

    Full description at Econpapers || Download paper

26. The Interdependencies between Economic Growth, Energy Consumption and Pollution in Europe. (2021). Cplescu, Raluca Dana ; Tvaronaviien, Manuela ; Dobrin, Cosmin ; Androniceanu, Ane-Mari.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:9:p:2577-:d:547046.

    Full description at Econpapers || Download paper

27. A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities. (2021). Drachal, Krzysztof ; Pawowski, Micha.
    In: Economies.
    RePEc:gam:jecomi:v:9:y:2021:i:1:p:6-:d:483079.

    Full description at Econpapers || Download paper

28. 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. (2021). Yuan, Shan ; Cheng, Ming ; Cao, Junxing ; Wang, Jun.
    In: Energy.
    RePEc:eee:energy:v:233:y:2021:i:c:s036054422101330x.

    Full description at Econpapers || Download paper

29. A decomposition-ensemble model with regrouping method and attention-based gated recurrent unit network for energy price prediction. (2021). Xu, Kunliang ; Niu, Hongli ; Liu, Cheng.
    In: Energy.
    RePEc:eee:energy:v:231:y:2021:i:c:s0360544221011890.

    Full description at Econpapers || Download paper

30. Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns. (2021). Ali, Aliyuda.
    In: Energy.
    RePEc:eee:energy:v:229:y:2021:i:c:s0360544221008975.

    Full description at Econpapers || Download paper

31. A novel method for online real-time forecasting of crude oil price. (2021). Zhao, Yuan ; Gong, Xue ; Zhang, Weiguo ; Wang, Chao.
    In: Applied Energy.
    RePEc:eee:appene:v:303:y:2021:i:c:s0306261921009648.

    Full description at Econpapers || Download paper

32. Multi-source Model of Heterogeneous Data Analysis for Oil Price Forecasting. (2021). Baboshkin, Pavel ; Uandykova, Mafura.
    In: International Journal of Energy Economics and Policy.
    RePEc:eco:journ2:2021-02-46.

    Full description at Econpapers || Download paper

33. Forecasting Crude Oil Price Using Event Extraction. (2021). Huang, Xiaohong ; Liu, Jiangwei.
    In: Papers.
    RePEc:arx:papers:2111.09111.

    Full description at Econpapers || Download paper

34. A novel hybrid model for forecasting crude oil price based on time series decomposition. (2020). Abdollahi, Hooman.
    In: Applied Energy.
    RePEc:eee:appene:v:267:y:2020:i:c:s030626192030547x.

    Full description at Econpapers || Download paper

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