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

  • Ricardo de A. Araújo
  • Peter Sussner
Conference paper

DOI: 10.1007/978-3-642-13803-4_44

Volume 6077 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
de A. Araújo R., Sussner P. (2010) An Increasing Hybrid Morphological-Linear Perceptron with Evolutionary Learning and Phase Correction for Financial Time Series Forecasting. In: Corchado E., Graña Romay M., Manhaes Savio A. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science, vol 6077. Springer, Berlin, Heidelberg

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.

Keywords

Lattice Theory Minimax Algebra Morphological Neural Networks Genetic Algorithms Financial Time Series Forecasting 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ricardo de A. Araújo
    • 1
  • Peter Sussner
    • 2
  1. 1.Information Technology Department[gm]2 Intelligent SystemsBrazil
  2. 2.Department of Applied MathematicsUniversity of CampinasBrazil