General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • E-mail:
    • Journal Metrics:

Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2017 Vol.9(5): 334-338 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2017.V9.1162

Analyzing Price Movement of Crude Oil through Historical Price Data Distribution by Using Apriori Data Mining Algorithm

Kwan-Hua Sim, Nicholas Ching-Yun Bong, and Kwan-Yong Sim

Abstract—Abstract—Volatile crude oil prices have drawn serious attention lately due to its enormous impact on both economically and politically stability of every oil producing countries in the world. The recent severe plunge of crude oil has immensely tampered the economy of countries that rely excessively on the export of crude oil and natural gas. Highly fluctuated crude oil price has been a major challenge haunting not only businesses, but also governmental agencies in their decision making process concerning risk management and mitigation against possible severe price fluctuation. Therefore it is imperative for exceptional price volatility of crude oil to be studied since conventional financial time series analysis and modeling techniques are inadequate in handling exceptional price volatility. This paper presents a historical crude oil price data distribution analysis by mining the association rules between the characteristic of price distribution and the subsequent maximum price movement. Experiment was conducted on the historical price data of crude oil futures for the period of thirty years to explore the possible association rules by using Apriori data mining algorithm. Evaluation and analysis are performed on the best rules minded to scrutinize each characteristic range of historical price distribution and the maximum future price movement. The outcomes of the experiments reveal a convincing level of association between higher and average downward price movements of crude oil with the historical price distribution that demonstrates positive skewness value. This study institutes a new way of analyzing historical price data to gain information and insight from the distribution of historical data set; the finding stimulates an innovative way on how price data can be interpreted to derive information that is associated to the future price movement of crude oil.

Index Terms—Index Terms—Data mining, financial time series, price distribution, statistical analysis.

Kwan-Hua Sim, Nicholas Ching-Yun Bong, and Kwan-Yong Sim are with Swinburne University of Technology Sarawak Campus, Jalan Simpang Tiga, Kuching 93350 Malaysia (e-mail:,,


Cite:Kwan-Hua Sim, Nicholas Ching-Yun Bong, and Kwan-Yong Sim, "Analyzing Price Movement of Crude Oil through Historical Price Data Distribution by Using Apriori Data Mining Algorithm," International Journal of Computer Theory and Engineering vol. 9, no. 5, pp. 334-338, 2017.

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