Computational Intelligence Techniques With Application to Crude Oil Price Projection: A Literature Survey From 2001–2012

Chiroma, Haruna; Kareem, Sameem Abdul; Abubakar, Adamu; Usman, Mohammed

doi:10.14311/nnw.2013.23.032

Cited by 17 publications

(6 citation statements)

References 78 publications

(78 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, these individual techniques have produced great accuracy in forecasting oil prices compared with traditional econometric methods. A rich bibliographic synthesis regarding applications of ANNs and computational intelligence techniques in forecasting the price of crude oil can be found in Hamdi and Aloui (2015) and Chiroma et al (2013). By focusing on the review of the literature related to this topic, we can deduce that ANNs models have been the most widely used to forecast the crude oil price in the last decade.…”

Section: Related Literature Reviewmentioning

confidence: 99%

Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

Aras

Hamdi

2019

SAEB

View full text Add to dashboard Cite

When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test and compare its performance with two general methods, namely, using all available data and using just two years of data. Furthermore, the importance of the selection of the final neural network model is investigated in detail. The results obtained from daily crude oil prices indicate that the data from the last structural change lead to simpler architectures of neural networks and have an advantage in reaching more accurate forecasts in terms of MAE value. In addition, the statistical tests show that there is a statistically significant interaction between data size and stopping rule.

show abstract

Section: Related Literature Reviewmentioning

confidence: 99%

Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

Aras

Hamdi

2019

SAEB

View full text Add to dashboard Cite

show abstract

“…The trial-and-error method commonly used in the literature [52] was used for the selection of PSO and GA parameters: For PSO method, number of particles = 25; position constant learning rate 1 = 2 = 2; maximum iteration = 150; and constriction factor = 0.4. For GA method, number of individuals = 25; crossover rate = 0.5; mutation rate = 0.1; maximum generation = 150; also the type of operators used for each population in GA is linear ranking selection algorithm, simple crossover, and uniform mutation.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Genetic Algorithm and Particle Swarm Optimization Based Cascade Interval Type 2 Fuzzy PD Controller for Rotary Inverted Pendulum System

Hamza

Yap

Choudhury

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper presents the design of an optimized Interval Type 2 Fuzzy Proportional Derivative Controller (IT2F-PDC) in cascade form for Rotary Inverted Pendulum (RIP) system. The parameters of the IT2F-PDC are optimised by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The goal is to balance the pendulum in upright unstable equilibrium position. The IT2F-PDC which is the extended version of conventional type 1 fuzzy logic controller, improves the control strategy by using the advantage of its footprint of uncertainty for the fuzzy membership function. The performance characteristics considered for the controller are steady state error, settling time, rise time, maximum overshoot, and control energy. Experimental and simulation results indicated that the effectiveness and robustness of the proposed GA- and PSO-based controllers on the RIP with respect to load disturbances, parameter variation, and noise effects have been improved over state-of-the-art method. However, the comparative results for simulation and experiment based on cascade IT2F-PDC indicate that GA-based IT2F-PDC has lower steady state error while PSO-based IT2F-PDC has lower overshoot, settling time, and control energy, but both have almost the same rise time. The proposed control strategy can be regarded as a promising strategy for controlling different unstable and nonlinear systems.

show abstract

“…The dataset for the OPEC CO 2 emissions from the consumption of petroleum in million metric tons (mmt) from 1980 to 2011 was collected from [ 49 ], a credible source of energy data [ 50 ]. The data are collected yearly, in view of the fact that the data are available on a yearly basis.…”

Section: The Organization Of the Petroleum Exporting Countries’ Co mentioning

confidence: 99%

Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

et al. 2015

Self Cite

View full text Add to dashboard Cite

BackgroundGlobal warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research.Methods/FindingsThe OPEC CO2 emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. The proposed model predicts OPEC CO2 emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods.ConclusionAn accurate prediction of OPEC CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks—hence, reducing global warming. The policy implications are discussed in the paper.

show abstract

Computational Intelligence Techniques With Application to Crude Oil Price Projection: A Literature Survey From 2001–2012

Cited by 17 publications

References 78 publications

Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

Genetic Algorithm and Particle Swarm Optimization Based Cascade Interval Type 2 Fuzzy PD Controller for Rotary Inverted Pendulum System

Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

Contact Info

Product

Resources

About