Research on the Prediction of the Global Price of WTI Crude
DOI:
https://doi.org/10.54097/42r5tn50Keywords:
Prediction; WTI crude; ARIMA model.Abstract
As the cornerstone of industrial development, crude oil prices have attracted international attention. However, due to the frequency of price fluctuations and the sensitivity to the international economic situation, crude oil prices are difficult to predict. Due to significant differences in prices before and after 2007, this will increase the difficulty of prediction and decrease its accuracy. The ARIMA model, as a typical prediction model, is highly favored by scholars from various countries due to its accuracy and adaptability. This article will use the ARIMA model to forecast the one-year future prices. The conclusion is as follows: in the two months, the prices will have a slight upward trend, while in the next year, the overall trend will show a relatively obvious downward trend. Therefore, this article believes that it is not recommended to invest in crude oil in the short term. At the same time, this article believes that ensuring domestic economic stability can reduce the impact of international economic shocks and thus gain an advantageous position in international trade.
Downloads
References
Zhang Y J, Wu Y B. The dynamic information spill-over effect of WTI crude oil prices on China’s traditional energy sectors. China Agricultural Economic Review, 2018, 10(3): 516-534.
Irwin S H, Sanders D R, Merrin R P. Devil or angel? The role of speculation in the recent commodity price boom (and bust). Journal of Agricultural and Applied Economics, 2009, 41(2): 377-391.
Shaikh I. On the relation between the crude oil market and pandemic Covid-19. European Journal of Management and Business Economics, 2021, 30(3):331-356.
Pellejero S. Oil prices fall as rising COVID-19 cases prompt demand concerns. Investors Also Eye Rising Crude, 2020.
Kadhem S, Thajel H. Modelling of crude oil price data using hidden Markov model. Journal of Risk Finance, 2023, 24(2): 269-284.
Pan S, Li H, Wang Y, Cai W. Crude oil price prediction with LSTM neural networks. Computer Technology and Development, 2021, 31(5): 180-185.
Pan W. Mixed modified fruit fly optimization algorithm with general regression neural network to build oil and gold prices forecasting model. Kybernetes, 2014, 43(7): 1053-1063.
Wu Y X, Wu Q B, Zhu J Q. Improved EEMD-based crude oil price forecasting using LSTM networks. Physica A: Statistical Mechanics and its Applications, 2019, 516(15): 114-124.
Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proceedings of The Royal Society A, 1998, 454: 903-995.
María E T, Marcelo A C, Gaston S, et al. A complete ensemble empirical mode decomposition with adaptive noise. Prague, Czech Republic, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011: 4144-4147.
Downloads
Published
Conference Proceedings Volume
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.