Abstract
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.
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de A. Araújo, R., Oliveira, A.L.I., Meira, S.R.L. (2012). A Dilation-Erosion-Linear Perceptron for Bovespa Index Prediction. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_50
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DOI: https://doi.org/10.1007/978-3-642-32639-4_50
Publisher Name: Springer, Berlin, Heidelberg
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