文章基本信息

标题：Gasoline Price Forecasting: An Application of LSSVM with Improved ABC
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作者：Zuriani Mustaffa ; Zuriani Mustaffa ; Yuhanis Yusof 等
期刊名称：Procedia - Social and Behavioral Sciences
印刷版ISSN：1877-0428
出版年度：2014
卷号：129
页码：601-609
DOI：10.1016/j.sbspro.2014.03.718
语种：English
出版社：Elsevier
摘要：AbstractOptimizing the hyper-parameters of Least Squares Support Vector Machines (LSSVM) is crucial as it will directly influence the predictive power of the algorithm. To tackle such issue, this study proposes an improved Artificial Bee Colony (IABC) algorithm which is based on conventional mutation. The IABC serves as an optimizer for LSSVM. Realized in gasoline price forecasting, the performance is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE). The conducted simulation results show that, the proposed IABC- LSSVM outperforms the results produced by ABC-LSSVM and also the Back Propagation Neural Network.
关键词：Least Squares Support Vector Machines;Artificial Bee Colony;Price forecasting.