An Increasing Hybrid Morphological-Linear Perceptron with Evolutionary Learning and Phase Correction for Financial Time Series Forecasting

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Abstract

In this paper we present a suitable model to solve the financial time series forecasting problem, called increasing hybrid morphological-linear perceptron (IHMP). An evolutionary training algorithm is presented to design the IHMP (learning process), using a modified genetic algorithm (MGA). The learning process includes an automatic phase correction step that is geared at eliminating the time phase distortions that typically occur in financial time series forecasting. Furthermore, we compare the proposed IHMP with other neural and statistical models using two complex nonlinear problems of financial forecasting.