Abstract
Crude oil is one of the main commodities traded on international commodity markets. Price fluctuations of this raw material are influenced by external factors such as political events, weather factors, but also wars and so-called demand and supply shocks. The activities of the OPEC organization are also important. In commodity trade, there are two main types of crude oil, the so-called WTI and Brent. It seems that there is a lack of direct comparison of the PROPHET model with XGBoost and verification of both models for spot prices for both WTI and Brent crude oil. In addition to the aforementioned models also LSTM model was also tested. The article aims to compare the predictive capabilities of the Prophet and XGBoost models as well as LSTM in predicting crude oil spot prices.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Atanu, E., Etuk, E., Amos, E.: Comparative performance of ARIMA and GARCH model in forecasting crude oil price data. Asian J. Probab. Stat. (2021). https://doi.org/10.9734/ajpas/2021/v15i430378
Aziz, M., Barawi, M., Shahiri, H.: Is Facebook PROPHET superior than hybrid ARIMA model to forecast crude oil price? Universiti Kebangsaan Malaysia (2022)
Baumeister, C., Kilian, L.: Real-time forecasts of the real price of oil. J. Bus. Econ. Stat. 30, 326–336 (2012). https://doi.org/10.1080/07350015.2011.648859
Butler, S., Kokoszka, P., Miao, H., Shang, H.L.: Neural network prediction of crude oil futures using B-splines. Energy Econ. 94, 105080 (2021). https://doi.org/10.1016/j.eneco.2020.105080
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD, pp. 785–794 (2016)
Chong, T.T.L., Ng, W.K.: Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30. Appl. Econ. Lett. 15(14), 1111–1114 (2008)
Gumus, M., Kiran, M.S.: Crude oil price forecasting using XGBoost. In: 2017 International Conference on Computer Science and Engineering (UBMK). IEEE (2017). https://doi.org/10.1109/UBMK.2017.8093500
Haque, M.I., Rahman, A.: Predicting crude oil prices during a pandemic: a comparison of arima and garch models. Montenegrin J. Econ. 17(1), 197–207 (2021). https://doi.org/10.14254/1800-5845/2021.17-1.15
How to Use XGBoost for Time Series Forecasting. https://machinelearningmastery.com/xgboost-for-time-series-forecasting/. Accessed 25 Oct 2023
Jurafsky, D., Martin, J.H.: Speech and Language Processing. (3rd ed. Draft, Oct. 2019). https://web.stanford.edu/~jurafsky/slp3/. Accessed 05 Oct 2023
Kilian, L.: Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. Am. Econ. Rev. 99(3), 1053–1069 (2009)
Lu, Q., Sun, S., Hongbo Duan, H., Shouyang Wang, S.: Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model. In: Proceedings of the Energy Informatics. Academy Conference Asia 2021. Energy Informatics. Springer, Cham (2021)
Manowska, A., Bluszcz, A.: Forecasting crude oil consumption in Poland based on LSTM recurrent neural network. Energies 15(13), 4885 (2022). https://doi.org/10.3390/en15134885
PROPHET. https://facebook.github.io/prophet/. Accessed 10 Nov 2023
Sepp, H.: Untersuchungen zu dynamischen neuronalen Netzen (PDF) (diploma thesis). Technical University Munich, Institute of Computer Science (1991)
Sutskever, I.: Training recurrent neural networks. (PhD thesis). University of Toronto (2013). hdl:1807/36012
Tian, G., Peng, Y., Meng, Y.: Forecasting crude oil prices in the COVID-19 era: can machine learn better? Energy Econ. 125, 106788 (2023). https://doi.org/10.1016/j.eneco.2023.106788
XGBoost Documentation. https://xgboost.readthedocs.io/en/stable/tutorials/model.html. Accessed 15 Oct 2023
Xiang, Y.: Using ARIMA-GARCH Model to Analyze Fluctuation Law of International Oil Price. Mathematical Problems in Engineering Hindawi, pp. 1–6, United Kingdom (2022). https://doi.org/10.1155/2022/3936414
Zhang, Y.: Stock price prediction method based on XGboost algorithm. In: Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022). https://doi.org/10.2991/978-94-6463-030-5_60
Zhang, K., Hong, M.: Forecasting crude oil price using LSTM neural networks. Data Sci. Finan. Econ. 2(3), 163–180 (2022). https://doi.org/10.3934/DSFE.2022008
https://plantmethods.biomedcentral.com/articles/10.1186/s13007-023-01044-8
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ziółkowski, K. (2024). Forecasting WTI & Brent Crude Oil Price Using LSTM, Prophet and XGBoost – Comparative Analysis. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2024. Communications in Computer and Information Science, vol 2145. Springer, Singapore. https://doi.org/10.1007/978-981-97-5934-7_15
Download citation
DOI: https://doi.org/10.1007/978-981-97-5934-7_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5933-0
Online ISBN: 978-981-97-5934-7
eBook Packages: Computer ScienceComputer Science (R0)