Modelling Oil Price with Lie Algebras and Long Short-Term Memory Networks

Melike Bildirici, Nilgun Guler Bayazit and Yasemen Ucan
Additional contact information
Nilgun Guler Bayazit: Department of Mathematical Engineering, Yildiz Technical University, 34220 Istanbul, Turkey
Yasemen Ucan: Department of Mathematical Engineering, Yildiz Technical University, 34220 Istanbul, Turkey

Mathematics, 2021, vol. 9, issue 14, 1-10

Abstract: In this paper, we propose hybrid models for modelling the daily oil price during the period from 2 January 1986 to 5 April 2021. The models on S 2 manifolds that we consider, including the reference ones, employ matrix representations rather than differential operator representations of Lie algebras. Firstly, the performance of Lie NLS model is examined in comparison to the Lie-OLS model. Then, both of these reference models are improved by integrating them with a recurrent neural network model used in deep learning. Thirdly, the forecasting performance of these two proposed hybrid models on the S 2 manifold, namely Lie-LSTM OLS and Lie-LSTM NLS , are compared with those of the reference Lie OLS and Lie NLS models. The in-sample and out-of-sample results show that our proposed methods can achieve improved performance over Lie OLS and Lie NLS models in terms of RMSE and MAE metrics and hence can be more reliably used to assess volatility of time-series data.

Keywords: oil price forecasting; Lie group SO(3); LSTM; deep learning; short-term model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/9/14/1708/pdf (application/pdf)
https://www.mdpi.com/2227-7390/9/14/1708/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:9:y:2021:i:14:p:1708-:d:597836

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().