Predicting Crude Oil Price Using Fuzzy Rough Set and Bio-Inspired Negative Selection Algorithm

Predicting Crude Oil Price Using Fuzzy Rough Set and Bio-Inspired Negative Selection Algorithm

Ayodele Lasisi (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia), Nasser Tairan (College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia), Rozaida Ghazali (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia), Wali Khan Mashwani (Department of Mathematics, Kohat University of Science and Technology, Kohat, Pakistan), Sultan Noman Qasem (Computer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia & Computer Science Department, Faculty of Applied Sciences, Taiz University, Taiz, Yemen), Harish Kumar G R (College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia) and Anuja Arora (Jaypee Institute of Information Technology, Noida, India)
Copyright: © 2019 |Pages: 13
DOI: 10.4018/IJSIR.2019100102

Abstract

The need to accurately predict and make right decisions regarding crude oil price motivates the proposition of an alternative algorithmic method based on real-valued negative selection with variable-sized detectors (V-Detectors), by incorporating with fuzzy-rough set feature selection (FRFS) for predicting the most appropriate choices. The objective of this study is enhancing the performance of V-Detectors using FRFS for prices of crude oil. Applying FRFS serves to prune the number of features by retaining the most informative and critical features. The V-Detectors then trains and tests the features. Different radius values are applied for V-Detectors. Experimental outcome in comparison with established algorithms such as support vector machine, naïve bayes, multi-layer perceptron, J48, non-nested generalized exemplars, IBk, fuzzy-roughNN, and vaguely quantified nearest neighbor demonstrates that FRFS-V-Detectors is proficient and valuable for insightful knowledge on crude oil price. Thus, it can assist in establishing oil price market policies on the international scale.
Article Preview
Top

Introduction

Aided by the scientific progression as well as continuing growth of worldwide global financial incorporation, the demand and supply involving crude oil are determined by escalating sophisticated and various economic contributors globally. This situation, combined with a lot of significant components, which include climate, governmental consistency, economic potentials, consumer anticipations, along with business indicators, appears to have resulted in the varying price activity in the crude oil market. It thus tends to have increased recently, as demonstrated by the activities in benchmark indices, such as the West Texas Intermediate (WTI) and Brent crude oil prices, in association with the trend connected with liberalization and also globalization that happens to be beyond the informative expertise offered by the existing models and techniques. At the same time, because crude oil is actually traded less often as compared to equities, this led to greater degrees of market flaws and comparatively lesser degrees of effectiveness. This results in theoretically important and demanding research issues (Zhu et al., 2014). There was stability in the market prices of crude oil as at early 2000, however, fluctuation changes in the market began to intensify and accelerate afterwards and has not subsided ever since (Askari & Krichene, 2008; He et al., 2016).

Due to these fluctuations in the price of crude oil, experts from the industry and academics have engineered and constructed models as well as techniques in accurately predicting and forecasting the crude oil prices as a way of regulating its movements. The engineering of evolutionary and data mining techniques provided the flexibility of diffusing problems arising with crude oil price. Products of such evolutionary techniques are algorithms that mimic the Biological Immune System (BIS) known as the Artificial Immune System (AIS). Assessment of AIS algorithms are expatiated in Dasgupta et al. (2011). Several other evolutionary algorithms are particle swarm optimization (PSO) (Garg, 2014a; Garg et al., 2014), artificial bee colony (ABC) (Shah et al., 2012; Shah et al., 2014; Garg, 2014b), cuckoo search (CS) algorithm (Garg, 2015b), and biogeography-based optimization (BBO) algorithm (Garg, 2015c). With respect to the aforementioned algorithms, a hybrid gravitational search algorithm (GSA) together with genetic algorithm (GA) was developed for both unconstraint optimization and industrial uncertain data usage (Garg, 2015a; 2019). For the prediction of stock market prices, Shah et al. (2018) came up with a quick gbest guided artificial bee colony as a training process for feed forward neural network termed (QGGABC-FFNN).

The issues arising from the above instability generated from prices of crude oil motivated this research. This study thus proposes the use of Negative Selection Algorithm (NSA) specifically the Variable-Sized Detectors (V-Detectors) (Ji & Dasgupta, 2004; Lasisi et al., 2016), coupled with Fuzzy Rough Feature Selection (FRFS) (Jensen & Shen, 2009), in predicting crude oil price. The purpose of the Fuzzy Rough Feature Selection (FRFS) is to select the best attributes from the crude oil dataset. The reason for selecting the best features is for identifying the most useful and relevant attributes to predicting crude oil price. The selected features from FRFS is consequently trained and tested with the V-Detectors algorithm which aid the performance of the prediction outcome. The objective of this study is to trigger a boost in the performance of V-Detectors via the use of FRFS in selecting best features that represents the overall crude oil price dataset.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 11: 4 Issues (2020): 2 Released, 2 Forthcoming
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing