Training LSSVM with GWO for price forecasting

This paper presents a hybrid forecasting model namely Grey Wolf Optimizer-Least Squares Support Vector Machines (GWO-LSSVM). In this study, a great deal of attention was paid in determining LSSVM's hyper parameters. For that matter, the GWO is utilized an optimization tool for optimizing the said hyper parameters. Realized in gold price forecasting, the feasibility of GWO-LSSVM is measured based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE). Upon completing the simulation tasks, the comparison against two hybrid methods suggested that the GWO-LSSVM capable to produce lower forecasting error as compared to the identified forecasting techniques.

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