Forecasting WTI & Brent Crude Oil Price Using LSTM, Prophet and XGBoost - Comparative Analysis

被引:0
作者
Ziolkowski, Krzysztof [1 ]
机构
[1] WSB Merito Univ Gdansk, Aleja Grunwaldzka 238A, PL-80266 Gdansk, Poland
来源
RECENT CHALLENGES IN INTELLIGENT INFORMATION AND DATABASE SYSTEMS, PT II, ACIIDS 2024 | 2024年 / 2145卷
关键词
LSTM; Prophet; XGBoost; WTI & Brent spot price;
D O I
10.1007/978-981-97-5934-7_15
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 alsowars 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-calledWTI 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.
引用
收藏
页码:171 / 181
页数:11
相关论文
共 22 条
[1]  
[Anonymous], 2013, Training recurrent neural networks
[2]  
[Anonymous], 2021, XGBoost documentation
[3]  
Atanu E., 2021, Asian J. Probab. Stat, DOI [10.9734/ajpas/2021/v15i430378, DOI 10.9734/AJPAS/2021/V15I430378]
[4]  
Aziz M., 2022, Is Facebook PROPHET superior than hybrid ARIMA model to forecast crude oil price?
[5]   Real-Time Forecasts of the Real Price of Oil [J].
Baumeister, Christiane ;
Kilian, Lutz .
JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2012, 30 (02) :326-336
[6]  
biomedcentral, About us, DOI [10.1186/s13007-023-01044-8, DOI 10.1186/S13007-023-01044-8]
[7]   Neural network prediction of crude oil futures using B-splines [J].
Butler, Sunil ;
Kokoszka, Piotr ;
Miao, Hong ;
Shang, Han Lin .
ENERGY ECONOMICS, 2021, 94
[8]   XGBoost: A Scalable Tree Boosting System [J].
Chen, Tianqi ;
Guestrin, Carlos .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :785-794
[9]   Technical analysis and the London stock exchange: testing the MACD and RSI rules using the FT30 [J].
Chong, Terence Tai-Leung ;
Ng, Wing-Kam .
APPLIED ECONOMICS LETTERS, 2008, 15 (14) :1111-1114
[10]  
Gumus M, 2017, 2017 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ENGINEERING (UBMK), P1100, DOI 10.1109/UBMK.2017.8093500