Particle swarm optimization of ensemble neural networks with fuzzy aggregation for time series prediction of the Mexican Stock Exchange

This paper describes a hybrid method based on particle swarm optimization for designing ensemble neural networks with fuzzy aggregation of responses to forecast complex time series. The time series that was considered in this paper, to compare the hybrid approach with traditional methods, is the Mexican Stock Exchange, and the results shown are for the optimization of the structure of the ensemble neural network with type-1 and type-2 fuzzy logic integration. Simulation results show that the optimized ensemble neural network approach produces good prediction of the Mexican Stock Exchange.