Crude oil price prediction using temporal fusion transformer model

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

View graph of relations

Author(s)

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

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)927-932
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

In this paper, we applied the temporal fusion transformer model to the crude oil price movement modeling and forecasting. The temporal fusion transformer model has been adopted in the crude oil price forecasting model using the attention mechanism, to capture the different level of autocorrelations among observations in the crude oil prices. Empirical evaluation of the transformer based multi-horizon ahead crude oil price forecasting model has been conducted. Experiment results show that the introduction of transformer model in the forecasting process has improved the forecasting accuracy significantly at longer time horizon. © 2023 The Authors. Published by Elsevier B.V.

Research Area(s)

  • ARIMA, Crude oil price forecasting, Temporal Fusion Transformer model

Citation Format(s)

Crude oil price prediction using temporal fusion transformer model. / He, Kaijian; Zheng, Linyuan; Yang, Qian et al.
In: Procedia Computer Science, Vol. 221, 2023, p. 927-932.

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

Download Statistics

No data available