Abstract
This paper presents a morphological-linear model, called the dilation-erosion-linear perceptron (DELP), for financial forecasting. It consists of a hybrid model 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 S&P500 Index, where five well-known performance metrics and an evaluation function are used to assess the forecasting performance.
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© 2012 Springer-Verlag Berlin Heidelberg
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de A. Araújo, R., Oliveira, A.L.I., Meira, S.R.L. (2012). A Hybrid Model for S&P500 Index Forecasting. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_71
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DOI: https://doi.org/10.1007/978-3-642-33266-1_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33265-4
Online ISBN: 978-3-642-33266-1
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