Crude oil price prediction using temporal fusion transformer model
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
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Detail(s)
Original language | English |
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Pages (from-to) | 927-932 |
Journal / Publication | Procedia Computer Science |
Volume | 221 |
Online published | 10 Aug 2023 |
Publication status | Published - 2023 |
Conference
Title | 10th International Conference on Information Technology and Quantitative Management (ITQM 2023) |
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Place | United Kingdom |
City | Oxford |
Period | 12 - 14 August 2023 |
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DOI | DOI |
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Attachment(s) | Documents
Publisher's Copyright Statement
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85171742317&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(aa0cf685-0eff-47a8-846c-719790d3965d).html |
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.
In: Procedia Computer Science, Vol. 221, 2023, p. 927-932.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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