Forecasting WTI & Brent Crude Oil Price Using LSTM, Prophet and XGBoost – Comparative Analysis
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5934-7_15
Reference22 articles.
1. 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
2. Aziz, M., Barawi, M., Shahiri, H.: Is Facebook PROPHET superior than hybrid ARIMA model to forecast crude oil price? Universiti Kebangsaan Malaysia (2022)
3. 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
4. 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
5. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD, pp. 785–794 (2016)
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