A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series

Seçkin Karasu, Aytaç Altan, Stelios Bekiros and Wasim Ahmad

Energy, 2020, vol. 212, issue C

Abstract: Forecasting the future price of crude oil, which has an important role in the global economy, is considered as a hot matter for both investment companies and governments. However, forecasting the price of crude oil with high precision is indeed a challenging task because of the nonlinear dynamics of the crude oil time series, including chaotic behavior and inherent fractality. In this study, a new forecasting model based on support vector regression (SVR) with a wrapper-based feature selection approach using multi-objective optimization technique is developed to deal with this challenge. In our model, features based on technical indicators such as simple moving average (SMA), exponential moving average (EMA), and Kaufman’s adaptive moving average (KAMA) are utilized. SMA, EMA, and KAMA indicators are obtained from Brent crude oil closing prices under different parameters. The features based on SMA and EMA indicators are formed by changing the period values between 3 and 10. The features based on the KAMA indicator are obtained by changing the efficiency ratio (ER) period value, which is considered as fractality efficiency, between 3 and 10. The features are selected by the wrapper-based approach consisting of multi-objective particle swarm optimization (MOPSO) and radial basis function based SVR (RBFSVR) techniques considering both the mean absolute percentage error (MAPE) and Theil’s U values. The obtained empirical results show that the proposed forecasting model can capture the nonlinear properties of crude oil time series, and that better forecasting performance can be obtained in terms of precision and volatility than the other current forecasting models.

Keywords: Crude oil; Fractality; Volatility; Feature selection; Multi-objective particle swarm optimization (MOPSO); Support vector regression (SVR) (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (104)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544220318570
Full text for ScienceDirect subscribers only

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:eee:energy:v:212:y:2020:i:c:s0360544220318570

DOI: 10.1016/j.energy.2020.118750

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().