In this work we present the dilation-erosion-linear perceptron (DELP) for financial prediction. It is composed of morphological operators under context of lattice theory and a linear operator. A gradient-based method is presented to design the proposed DELP (learning process). Also, it is included an automatic phase fix procedure to adjust time phase distortions observed in financial phenomena. Furthermore, an experimental analysis is conducted with the proposed model using the Bovespa Index, where five well-known performance metrics and an evaluation function are used to assess the prediction performance. © 2012 Springer-Verlag.
CITATION STYLE
De A. Araújo, R., Oliveira, A. L. I., & Meira, S. R. L. (2012). A dilation-erosion-linear perceptron for Bovespa Index prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7435 LNCS, pp. 407–415). https://doi.org/10.1007/978-3-642-32639-4_50
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