Complex Neural Fuzzy Prediction Using Multi-Swarm Continuous Ant Colony Optimization

Abstract

Prediction of time series is one of major research subjects in data science. This paper proposes a novel approach to the problem of multiple-target prediction. The proposed approach is mainly composed of three parts: the complex neuro-fuzzy system (CNFS) built by using complex fuzzy sets, the two-stage feature selection method for multiple targets, and the hybrid machine learning method that uses the multi-swarm continuous ant colony optimization (MCACO) and the recursive least squares estimation (RLSE). The CNFS predictive model is responsible for prediction after training. During the training of the model, the parameters are updated by the MCACO method and the RLSE method where the two methods work cooperatively to become one machine learning procedure. For the predictive model, complex fuzzy sets (CFSs) are with complex-valued membership degrees within the unit disk of the complex plane, useful to the non-linear mapping ability of the CNFS model for multiple target prediction. This CFS property is contrast to real-valued membership degrees in the unit interval [0,1] of traditional fuzzy sets. The two-stage feature selection applies to select significant features to be the inputs to the model for multiple target prediction. Experiments using real world data sets obtained from stock markets for the prediction of multiple targets have been conducted. With the results and performance comparison, the proposed approach has shown outstanding performance over other compared methods.