Modes decomposition method in fusion with robust random vector functional link network for crude oil price forecasting

Abstract

This paper proposes a decomposition based method in fusion with the non-iterative approach for crude oil price forecasting. In this approach, the robust random vector functional link network (RVFLN), a non-iterative approach in fusion with the most efficient decomposition technique called variational mode decomposition (VMD) is proposed which is executed with two links — fixed assigned random weights and direct link from input to output, and the iterative learning process is not involved in its functioning which makes it faster in execution as compared to many existing techniques proposed for forecasting. The fusion of VMD and robust RVFLN called VMD-RVFLN is implemented for crude oil price forecasting where the crude oil price series is decomposed using VMD into a linear smoother series by extracting useful information and the decomposed modes pass through the robust RVFLN model which produces the final forecasting values. The analysis performed in the study approves its efficiency and reports improvement in forecasting accuracy and execution time as compared to some of the traditional iterative techniques like BPNN (back propagation neural network), ARIMA (auto-regressive integrated moving average), LSSVR (least squares support vector regression), ANFIS (adaptive neuro-fuzzy inference system), IT2FNN (interval type-2 fuzzy neural network) and RNN (recurrent neural network), etc. However, both ELM and RVFLN without modes decomposition fusion exhibit less execution time at the cost of reduction in prediction accuracy.