Forecasting crude oil futures prices using Extreme Gradient Boosting

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Scopus Citations
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Author(s)

  • Qian Yang
  • Kaijian He
  • Linyuan Zheng
  • Yi Yu
  • Yingchao Zou

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)920-926
Journal / PublicationProcedia Computer Science
Volume221
Online published10 Aug 2023
Publication statusPublished - 2023

Conference

Title10th International Conference on Information Technology and Quantitative Management (ITQM 2023)
PlaceUnited Kingdom
CityOxford
Period12 - 14 August 2023

Link(s)

Abstract

Multi-source data is widely used in the field of energy future prices forecasting, the improvement of forecast ability and data screening are becoming the focus of current research. In this paper, two tree-based models (namely, Random Forest and XGBoost model) are employed to predict China's crude oil future prices. The empirical analysis confirms that Random Forest and XGBoost model have superior prediction performances than benchmark and the XGBoost model performs best. An important finding is that there is a time gap between investor information search and processing because the prediction performance within the time lags is obviously superior than that of the current period. © 2023 The Author(s). Published by Elsevier B.V.

Research Area(s)

  • Oil futures forecasting, search index, XGBoost, random forest

Citation Format(s)

Forecasting crude oil futures prices using Extreme Gradient Boosting. / Yang, Qian; He, Kaijian; Zheng, Linyuan et al.
In: Procedia Computer Science, Vol. 221, 2023, p. 920-926.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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