Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model

Abdullah, S. N.; Zeng, Xiao‐Jun

doi:10.1109/ijcnn.2010.5596602

Cited by 45 publications

(32 citation statements)

References 10 publications

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“…Price of Crude Oil Researchers such as Abdullah in 2010 [12], Kantaporn et al [13], Arshad et al [10] confirmed the relationship between price of crude oil and CPO. Razak et al [14]employed the EngleGranger co-integration test to demonstrate a significant long term correlation between CPO price and crude oil price which was consistent with the recent work of Appalanaidu [15].…”

Section: Factors Influencing Cpo Pricementioning

confidence: 99%

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

Kanchymalay

Salim

Sukprasert

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

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Section: Factors Influencing Cpo Pricementioning

confidence: 99%

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

Kanchymalay

Salim

Sukprasert

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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“…It is important to analyses the probabilistic assumption of oil prices in terms of normality, linearity and serial correlation [4]. To forecast crude oil prices, a variety of approaches have been proposed by numerous authors employing time series [5][6][7][8][9][10], financial models [11,12] and structural models [13][14][15][16][17][18].…”

Section: Econometric Modelsmentioning

confidence: 99%

“…The study identifies that historical oil prices (either daily, weekly or yearly) are the most popular input variables used by researchers. Abdullah [18] has used 22 input variables from the categories as mentioned in Table 3 to achieve high prediction accuracy but the variables were selected on judgemental criteria. To handle optimal long-term oil price forecasting, Azadeh et al [64] developed a flexible algorithm based on artificial neural networks and fuzzy regression by using oil supply, crude oil distillation capacity, oil consumption of Non-OECD, USA refinery capacity and surplus capacity as economic indicators.…”

Section: Covers This In Detail) There Is No Solitary Indicatormentioning

confidence: 99%

“…Abdullah and Zeng [18] integrated 22 quantitative input variables (sub-factors of demand, supply, economy, inventory and population) together with the qualitative data (collected from experts' view and news) using neural networks to predict oil prices for long and short term time period. The authors have utilized manual feature extraction method for finding significant input variables for the study.…”

Section: Neural Network Based Modelsmentioning

confidence: 99%

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Artificial intelligence methods for oil price forecasting: a review and evaluation

Sehgal

Pandey

2015

Energy Syst

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Artificial intelligent methods are being extensively used for oil price forecasting as an alternate approach to conventional techniques. There has been a whole spectrum of artificial intelligent techniques to overcome the difficulties of complexity and irregularity in oil price series. The potential of AI as a design tool for oil price forecasting has been reviewed in this study. The following price forecasting techniques have been covered: (i) artificial neural network, (ii) support vector machine, (iii) wavelet, (iv) genetic algorithm, and (v) hybrid systems. In order to investigate the state of artificial intelligent models for oil price forecasting, thirty five research papers (published during 2001 to 2013) had been reviewed in form of table (for ease of comparison) based on the following parameters: (a) input variables, (b) input variables selection method, (c) data characteristics (d) forecasting accuracy and (e) model architecture. This review reveals procedure of AI methods used in complex oil price related studies. The review further extended above overview into discussions regarding specific shortcomings that are associated with feature selection for designing input vector, and then concluded with future insight on improving the current state-of-the-art technology.

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“…SampEn is even less sensitivity to the length of data sets and provides more consistent results which being also reliable to be used in small data sets (Chen et al, 2006;Ferrario et al, 2006;Lake et al, 2002;Yentes et al, 2013). It is important to note that ApEn and SampEn have also been used as a metric to evidence the chaotic behaviour of time series that presents positive λ (Cencini and Ginelli, 2013;Chen et al, 2016;Gaspard et al, 1998;Kaplan et al, 1991;Pincus, 1995;Sanei, 2013;Schreiber and Kantz, 1995) The chaotic behaviour of the price sequence of several mineral commodities has been assessed previously, however these studies (Abdullah and Zeng, 2010;He et al, 2015;Panas, 2001;Panas and Ninni, 2000) have been limited to sample periods of 15 years or less, and used daily or monthly prices. It is important to verify the time-related behaviour of mineral commodity prices in the longterm by using annual data, due to the impact of economic decisions, technological and regulatory changes which can be observed over years (Bernanke, 2013;Slade, 2015;Yellen, 2013).…”

Section: Introductionmentioning

confidence: 98%

Determining the chaotic behaviour of copper prices in the long-term using annual price data

et al. 2018

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Mineral commodity prices are influenced by economic, technological, psychological, and geopolitical factors. Stochastic approaches, and time series and econometric techniques have been used to represent the dynamics of mineral commodity markets and predict prices. However, these techniques cannot provide a comprehensive representation of market dynamics because they do not recognise the relationship between these factors over time, and they are unable to capture both the evolution and the cumulative effects of these factors on prices. Stability of motion and chaos theories can detect sensitivity to initial conditions, and therefore the evolutionary patterns allowing a proper understanding and representation of mineral commodity market dynamics. Most of the techniques used to assess chaos require a colossal amount of data, so the use of small data sets to assess chaos has been largely criticised. Nevertheless, by definition, the dynamics of a chaotic system remain at different scales owing to its self-organisation features that exhibit ordered patterns in the absence of codes or rules. Therefore, any deterministic chaotic behaviour of mineral commodity prices can be captured by using small data sets if a detailed qualitative and quantitative analysis are carried out. This paper examines the chaotic behaviour of annual copper prices between 1900 and 2015. To do so, we combine chaos theory, stability of motion and statistical techniques to reconstruct the long-term dynamics of copper prices. First, we examine the time dependency and the presence of a strange attractor by a visual analysis of the time series and phase space reconstruction based on Takens' theorem and determine embedding parameters. Then we examine the dynamic characteristics of the system which assesses its complexity and regularity patterns to measure the system's entropy. Finally, we calculate the largest Lyapunov exponent λ to assess the sensitivity to initial conditions and determine chaotic behaviour supported by a surrogate test. We find that annual copper prices have a chaotic behaviour embedded in a high-dimensional space and short time delay. The study suggests that copper prices exhibit only a single state of low prices, which fluctuate through transitional periods of high prices. It challenges the assertion that metal markets have fluctuated over four major super cycles and debate the adequacy of stochastic and econometric models for representing mineral commodity market behaviour. This study recommends that the use of chaotic behaviour improves our understanding of mineral commodity markets and narrows the data searching, processing and monitoring requirements for forecasting. Therefore, it improves the performance of traditional techniques for selecting key factors that influence the market dynamics, and may also be used to select the most suitable algorithm for forecasting prices.

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Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model

Abstract: Abstract-

Cited by 45 publications

References 10 publications

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

Artificial intelligence methods for oil price forecasting: a review and evaluation

Determining the chaotic behaviour of copper prices in the long-term using annual price data

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