Yusof, Yuhanis and Mustaffa, Zuriani and Kamaruddin, Siti Sakira (2014) Data normalization techniques in swarm-based forecasting models for energy commodity spot price. In: 3rd International Conference on Computer Engineering and Mathematical Sciences (ICCEMS 2014), 4-5 December 2014, Langkawi, Malaysia. (Unpublished)

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Abstract

Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Artificial Bee Colony, data normalization.price forecasting, Grey Wolf Optimizer.
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	School of Computing
Depositing User:	Dr. Yuhanis Yusof
Date Deposited:	25 Mar 2015 04:28
Last Modified:	26 May 2016 06:47
URI:	https://repo.uum.edu.my/id/eprint/13764

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