PRICE PREDICTIVE ANALYSIS MECHANISM UTILIZING GREY WOLF OPTIMIZER-LEAST SQUARES SUPPORT VECTOR MACHINES
作者： Mustaffa Zuriani , Sulaiman M. H.
DOI:
关键词: Algorithm 、 Mean squared error 、 Support vector machine 、 Data mining 、 Least squares 、 Selection (genetic algorithm) 、 Computer science 、 Swarm intelligence 、 Mean absolute percentage error 、 Evolutionary computation 、 Generalization
摘要: A good selection of Least Squares Support Vector Machines (LSSVM) hyper-parameters' value is crucial in order to obtain a promising generalization on the unseen data. Any inappropriate set hyper parameters would directly demote prediction performance LSSVM. In this regard, study proposes hybridization LSSVM with new Swarm Intelligence (SI) algorithm namely, Grey Wolf Optimizer (GWO). With such hybridization, hyper-parameters interest are automatically optimized by GWO. The GWO-LSSVM realized predictive analysis gold price and measured based two indices viz. Mean Absolute Percentage Error (MAPE) Root Square (RMSPE). Findings suggested that possess lower error rate as compared three comparable algorithms which includes models Evolutionary Computation (EC) algorithms.
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