A novel hybrid approach to forecast crude oil futures using intraday data
Journal article
Authors | Apergis, Nicholas, Manickavasagam, Jeevananthan and Visalakshmi, S. |
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Abstract | Prediction of oil prices is an implausible task due to the multifaceted nature of oil markets. This study presents two novel hybrid models to forecast WTI and Brent crude oil prices using combinations of machine learning and nature inspired algorithms. The first approach, MARSplines-IPSO-BPNN, Multivariate Adaptive Regression Splines (MARSPlines) find the important variables that affect crude oil prices. Then, the selected variables are fed into an Improved Particle Swarm Optimization (IPSO) method to obtain the best estimates of the parameters of the Backpropagation Neural Network (BPNN). Once these parameters are obtained, the variables are fed into the BPNN model to generate the required forecasts. The second approach, MARSplines-FPA-BPNN, generates the parameters of BPNN through the Flower Pollination Algorithm (FPA). The forecasting performance of these new models is compared to certain benchmark models. The findings document that the MARSplines-FPA-BPNN model performs better than the other competitive models. |
Keywords | Research Subject Categories::SOCIAL SCIENCES; Crude oil prices; Forecasting; Flower Pollination |
Year | 2020 |
Journal | Technological Forecasting and Social Change |
Publisher | Elsevier |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.techfore.2020.120126 |
Web address (URL) | http://hdl.handle.net/10545/625412 |
http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
hdl:10545/625412 | |
Publication dates | 04 Jun 2020 |
Publication process dates | |
Deposited | 24 Nov 2020, 16:00 |
Accepted | 11 May 2020 |
Rights | Attribution-NonCommercial-NoDerivatives 4.0 International |
Contributors | University of Derby, International Management Institute and Central University of Tamil Nadu |
File | File Access Level Open |
File | File Access Level Open |
File |
https://repository.derby.ac.uk/item/92459/a-novel-hybrid-approach-to-forecast-crude-oil-futures-using-intraday-data
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