Intraday stock price forecasting based on variational mode decomposition

This paper presents a hybrid predictive model for forecasting intraday stock prices. The proposed model hybridizes the variational mode decomposition (VMD) which is a new multiresolution technique with backpropagation neural network (BPNN). The VMD is used to decompose price series into a sum of variational modes (VM). The extracted VM are used to train BPNN. Besides, particle swarm optimization (PSO) is employed for BPNN initial weights optimization. Experimental results from a set of six stocks show the superiority of the hybrid VMD PSO BPNN predictive model over the baseline predictive model which is a PSO BPNN model trained with past prices. (c) 2015 Elsevier B.V. All rights reserved.