An evolutionary approach to design dilation-erosion perceptrons for stock market indices forecasting

Authors:
Ricardo Araujo;Adriano Oliveira;Sergio Soares;Silvio Meira
Affiliations:
Federal University of Pernambuco, Recife, Brazil;Federal University of Pernambuco, Recife, Brazil;Federal University of Pernambuco, Recife, Brazil;Federal University of Pernambuco, Recife, Brazil
Venue:
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Year:
2011

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Abstract

In this work we present an evolutionary learning process using the covariance matrix adaptation evolutionary strategy (CMAES) to design the dilation-erosion perceptron (DEP) for stock market indices forecasting. Also, we have included an automatic phase fix procedure (APFP) into proposed learning process to eliminate time phase distortions observed in some forecasting problems. The main advantage of the DEP model designed by our learning process, apart from its higher forecasting performance, is do not request any methodology to overcome the nondifferentiability of morphological operators needed into classical gradient-based learning process of the DEP model. Besides, we present an experimental analysis using two stock market indices, where five well-known performance metrics and an evaluation function are used to assess forecasting performance.