A dilation-erosion-linear perceptron for bovespa index prediction

Authors:
Ricardo de A. Araújo;Adriano L. I. Oliveira;Silvio R. L. Meira
Affiliations:
Informatics Center, Federal University of Pernambuco, Recife, PE, Brazil,Informatics Department, Federal Institute of Sertão Pernambuco, Ouricuri, PE, Brazil;Informatics Center, Federal University of Pernambuco, Recife, PE, Brazil;Informatics Center, Federal University of Pernambuco, Recife, PE, Brazil
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

Citing 3
Cited 0

Neural Networks Approach to the Random Walk Dilemma of Financial Time Series

Applied Intelligence
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks

Neural Processing Letters
An intelligent hybrid morphological-rank-linear method for financial time series prediction

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

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.