Forecasting of Commodity Future Index using a Hybrid Regression Model based on Support Vector Machine and Grey Wolf Optimization Algorithm

Abstract

The forecasting and investigation of finance time series data are hard, and are the most confounded works pertained with investor decision. In this paper, an economic derivative instrument for Multi Commodity Exchange (MCX) index of CRUDEOIL is estimated by utilizing forecasting models based on recently formulated artificial intelligence (AI) approaches. These approaches have been appeared to perform astoundingly well in different optimization problems. Specifically, a novel hybrid forecasting model is designed by combining the support vector machine (SVM) and grey wolf optimization (GWO) and it is named as hybrid SVM-GWO. The presented hybrid SVM-GWO model eliminates the user determined control parameter, which is needed for other AI techniques. The practicality and proficiency of the presented SVM-GWO regression method is evaluated by predicting the everyday close price of CRUDEOIL index traded in the MCX of India Limited. The exploratory outcomes depicts that the present hybrid SVM-GWO technique is viable and outperforms superior to the conventional SVM, hybrid SVM-TLBO and SVM-PSO regression models.