Intraday stock price forecasting based on variational mode decomposition

• A new multiresolution technique is applied to the problem of intraday stock price forecasting.
• Variational mode decomposition is employed to analyze stock prices.
• The predictive model is based on a neural network optimized by particle swarm intelligence and trained with extracted variational modes.
• Proposed model outperforms baseline model.

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.