An intelligent hybrid morphological-rank-linear method for financial time series prediction

摘要

In this paper the morphological-rank-linear time-lag added evolutionary forecasting (MRLTAEF) method is proposed in order to overcome the random walk dilemma for financial time series prediction. It consists of an intelligent hybrid model composed of a morphological-rank-linear (MRL) filter combined with a modified genetic algorithm (MGA), which searches for the particular time lags capable of a fine tuned characterization of the time series and for the estimation of the initial (sub-optimal) parameters of the MRL filter. Each individual of the MGA population is trained by the averaged least mean squares (LMS) algorithm to further improve the parameters of the MRL filter supplied by the MGA. Initially, the proposed MRLTAEF method chooses the most tuned predictive model for time series representation, and then performs a behavioral statistical test in the attempt to adjust time phase distortions that appear in financial time series. Experiments are conducted with the proposed MRLTAEF method using six real-world financial time series according to a group of relevant performance metrics and the results are compared to multilayer perceptron (MLP) networks, MRL filters and the previously introduced time-delay added evolutionary forecasting (TAEF) method.