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ORIGINAL RESEARCH
A Study on the Impact Mechanism of WTI Futures
Price Forecasting Considering Mutation Factors
1
School of Mathematics and Statistics, Ludong University,Yantai 264001, China
Submission date: 2024-03-09
Acceptance date: 2024-04-18
Online publication date: 2024-09-02
Publication date: 2025-01-09
Corresponding author
Pol. J. Environ. Stud. 2025;34(2):1755-1769
KEYWORDS
TOPICS
ABSTRACT
Crude oil futures price forecasting plays a crucial role in assessing energy demand, promoting
the development of renewable energy, formulating environmental protection policies, and achieving
a balance between economic development and environmental protection. Limited studies have focused on
exploring this phenomenon from the perspective of establishing or explaining the influence mechanism.
This paper conducts three sets of experiments based on existing theoretical studies. Specifically,
the control group solely employs price for prediction, the conventional group integrates conventional
factors onto this basis, and the mutation group further incorporates the influence mechanism of mutation
factors based on the conventional group. Comparative analysis of the experimental results between
the control and conventional groups reveals the underlying principles of how conventional factors
influence price trends, and the experiments between the conventional and mutation groups simulate
price directions during unexpected situations. The results demonstrate that prediction accuracy follows
the order of the mutation group, conventional group, and control group, thus validating the hypothesis
proposed in this paper. These research findings hold great significance for futures price prediction and
provide valuable insights for related theories.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
REFERENCES (30)
1.
UROLAGIN S., SHARMA N., DATTA T.K. A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting. *Energy*, 231, 120963, 2021. <https://doi.org/10.1016/j.ener...>.
2.
ZHAO L.T., MIAO J., QU S., CHEN X.H. A multi-factor integrated model for carbon price forecasting: market interaction promoting carbon emission reduction. *Science of The Total Environment*, 796, 149110, 2021. <https://doi.org/10.1016/j.scit...> PMid:34328877.
3.
ABDOLLAHI H., EBRAHIMI S.B. A new hybrid model for forecasting Brent crude oil price. *Energy*, 200, 117520, 2020. <https://doi.org/10.1016/j.ener...>.
4.
ABDOLLAHI H. A novel hybrid model for forecasting crude oil price based on time series decomposition. *Applied Energy*, 267, 115035, 2020. <https://doi.org/10.1016/j.apen...>.
5.
JIANG H., HU W., XIAO L., DONG Y. A decomposition ensemble-based deep learning approach for crude oil price forecasting. *Resources Policy*, 78, 102855, 2022. <https://doi.org/10.1016/j.reso...>.
6.
FENG J., LU X., LIU Y., WU J., HE J., CHEN Z., ZHAO Z. Foundation Settlement Prediction of High-Plateau Airport Based on Modified LSTM Model and BP Neural Network Model. *Polish Journal of Environmental Studies*, 33 (3), 2037, 2024. <https://doi.org/10.15244/pjoes...> PMid:19015719.
7.
WU J., MIU F., LI T. Daily crude oil price forecasting based on improved CEEMDAN, SCA, and RVFL: a case study in WTI oil market. *Energies*, 13 (7), 1852, 2020. <https://doi.org/10.3390/en1307...>.
8.
LU H., MA X., MA M., ZHU S. Energy price prediction using data-driven models: A decade review. *Computer Science Review*, 39, 100356, 2021. <https://doi.org/10.1016/j.cosr...>.
9.
CHAI J., XING L.M., ZHOU X.Y., ZHANG Z.G., LI J. Forecasting the WTI crude oil price by a hybrid-refined method. *Energy Economics*, 71, 114, 2018. <https://doi.org/10.1016/j.enec...>.
10.
GULIYEV H., MUSTAFAYEV E. Predicting the changes in the WTI crude oil price dynamics using machine learning models. *Resources Policy*, 77, 102664, 2022. <https://doi.org/10.1016/j.reso...>.
11.
TANG L., ZHANG C., LI L., WANG S. A multi-scale method for forecasting oil price with multi-factor search engine data. *Applied Energy*, 257, 114033, 2020. <https://doi.org/10.1016/j.apen...>.
12.
ZHOU J., WANG S. A carbon price prediction model based on the secondary decomposition algorithm and influencing factors. *Energies*, 14 (5), 1328, 2021. <https://doi.org/10.3390/en1405...>.
13.
LU W., LI J., WANG J., QIN L. A CNN-BiLSTM-AM method for stock price prediction. *Neural Computing and Applications*, 33 (10), 4741, 2021. <https://doi.org/10.1007/s00521...>.
14.
NWULU N.I. A decision trees approach to oil price prediction. *International Artificial Intelligence and Data Processing Symposium*, 1, 2017. <https://doi.org/10.1109/IDAP.2...>.
15.
LU Q., LI Y., CHAI J. Crude oil price analysis and forecasting: A perspective of "new triangle". *Energy Economics*, 87, 104721, 2020. <https://doi.org/10.1016/j.enec...>.
16.
ZHAO G., XUE M., CHENG L. A new hybrid model for multi-step WTI futures price forecasting based on self-attention mechanism and spatial-temporal graph neural network. *Resources Policy*, 85, 103956, 2023. <https://doi.org/10.1016/j.reso...>.
17.
DAI Z., KANG J., HU Y. Efficient predictability of oil price: The role of number of IPOs and US dollar index. *Resources Policy*, 74, 102297, 2021. <https://doi.org/10.1016/j.reso...>.
18.
ZHAO J. Exploring the influence of the main factors on crude oil price volatility: An analysis based on GARCH-MIDAS model with Lasso approach. *Resources Policy*, 79, 103031, 2022. <https://doi.org/10.1016/j.reso...>.
19.
MINION L., BANERJEE A. "I can feel the money going out of the window": How high energy prices evoke negative emotions in people with previous experience of homelessness. *Energy Research & Social Science*, 108, 103387, 2024. <https://doi.org/10.1016/j.erss...>.
20.
LIU Y., NIU Z., SULEMAN M.T., YIN L., ZHANG H. Forecasting the volatility of crude oil futures: The role of oil investor attention and its regime switching characteristics under a high-frequency framework. *Energy*, 238, 121779, 2022. <https://doi.org/10.1016/j.ener...>.
21.
MIAO H., RAMCHANDER S., WANG T., YANG D. Influential factors in crude oil price forecasting. *Energy Economics*, 68, 77, 2017. <https://doi.org/10.1016/j.enec...>.
22.
MA Y., JI Q., PAN J. Oil financialization and volatility forecast: Evidence from multidimensional predictors. *Journal of Forecasting*, 38 (6), 564, 2019. <https://doi.org/10.1002/for.25...>.
23.
WANG G., SHARMA P., JAIN V., SHUKLA A., SHABBIR M.S., TABASH M.I., CHAWLA C. The relationship among oil prices volatility, inflation rate, and sustainable economic growth: Evidence from top oil importer and exporter countries. *Resources Policy*, 77, 102674, 2022. <https://doi.org/10.1016/j.reso...>.
24.
ZHANG G., WANG T., LOU Y., GUAN Z., ZHENG H., LI Q., WU J. Analysis of China's Provincial-Level Carbon Peaking Pathways Based on LSTM Neural Networks. *Chinese Journal of Management Science*, 1, 2024.
25.
HAO Y., TIAN C. A Hybrid Framework for Carbon Trading Price Forecasting: Incorporating the Role of Multiple Influence Factors. *Journal of Cleaner Production*, 262, 120378, 2022. <https://doi.org/10.1016/j.jcle...>.
26.
WU Z., SHI J. Analysis and Trend Prediction of Energy Carbon Emissions Influencing Factors in Beijing Based on the STIRPAT and GM (1,1) Models. *Chinese Journal of Management Science*, 20 (S2), 803, 2012.
27.
ZHANG B., MING T. An Analysis of the Factors Influencing China's Provincial Economy on Land Use Carbon Emissions Based on a Decoupling Model: A Case Study of Sichuan Province from 1990 to 2020. *Polish Journal of Environmental Studies*, 33 (3), 2457, 2024. <https://doi.org/10.15244/pjoes...>.
28.
ZHANG J. Spatial Distribution of Green Total Factor Productivity in Chinese Agriculture and Analysis of Its Influencing Factors. *Polish Journal of Environmental Studies*, 33 (3), 2473, 2024. <https://doi.org/10.15244/pjoes...>.
29.
D'ORAZIO P. Charting the complexities of a post-COVID energy transition: emerging research frontiers for a sustainable future. *Energy Research & Social Science*, 108, 103365, 2024. <https://doi.org/10.1016/j.erss...>.
30.
AZUBIKE V.C., GATIESH M.M. The intricate goal of energy security and energy transition: Considerations for Libya. *Energy Policy*, 187, 114005, 2024. <https://doi.org/10.1016/j.enpo...>.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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