Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer

Zuriani, Mustaffa and Yuhanis, Yusof (2015) Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer. In: Proceedings of the International MultiConference of Engineers and Computer Scientist (IMECS 2015), 18-20 March 2015 , Hong Kong. pp. 25-30..

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The ability to model and perform decision making is an essential feature of many real-world applications including the forecasting of commodity prices. In this study, a forecasting model based on a relatively new Swarm Intelligence (SI) behaviour, namely Grey Wolf Optimizer (GWO), is developed for short term time series forecasting. The model is built upon data obtained from the West Texas Intermediate (WTI) crude oil and gasoline price. Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE). Results showed that the GWO model outperformed DE in both crude oil and gasoline price forecasting. Furthermore, the proposed GWO produces a better forecast for gasoline price as compared to the ABC model,, as well as being at par in crude oil. Such an achievement indicates that GWO may become a competitor in the domain of time series forecasting and would be useful for investors in planning their investment and projecting their profit.

Item Type: Conference or Workshop Item (Speech)
Additional Information: ISBN: 978-988-19253-2-9 ISSN: 2078-0958 (Print); 2078-0966 (Online)
Uncontrolled Keywords: time series forecasting, Grey Wolf Optimizer, Artificial Bee Colony, swarm intelligence, data mining
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 09 Apr 2015 04:36
Last Modified: 26 Feb 2018 07:58
URI: http://umpir.ump.edu.my/id/eprint/8968
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