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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Clements, M.P., Franses, P.H., Swanson, N.R.: Forecasting economic and financial time-series with non-linear models. International Journal of Forecasting 20, 169–183 (2004)
de A. Araújo, R.: Swarm-based hybrid intelligent forecasting method for financial time series prediction. Learning and Nonlinear Models 5(2), 137–154 (2007)
Ferreira, T.A.E., Vasconcelos, G.C., Adeodato, P.J.L.: A new intelligent system methodology for time series forecasting with artificial neural networks. Neural Processing Letters 28, 113–129 (2008)
Sitte, R., Sitte, J.: Neural networks approach to the random walk dilemma of financial time series. Applied Intelligence 16(3), 163–171 (2002)
Malkiel, B.G.: A Random Walk Down Wall Street, Completely Revised and Updated Edition. W. W. Norton & Company (April 2003)
de A. Araújo, R., Ferreira, T.A.E.: An intelligent hybrid morphological-rank-linear method for financial time series prediction. Neurocomputing 72(10-12), 2507–2524 (2009)
Pessoa, L.F.C., Maragos, P.: Neural networks with hybrid morphological rank linear nodes: a unifying framework with applications to handwritten character recognition. Pattern Recognition 33, 945–960 (2000)
Sousa, R.P., Carvalho, J.M., Assis, F.M., Pessoa, L.F.C.: Designing translation invariant operations via neural network training. In: Proc. of the IEEE Intl. Conference on Image Processing, Vancouver, Canada (2000)
Takens, F.: Detecting strange attractor in turbulence. In: Dold, A., Eckmann, B. (eds.) Dynamical Systems and Turbulence. Lecture Notes in Mathematics, vol. 898, pp. 366–381. Springer, New York (1980)
Prechelt, L.: Proben1: A set of neural network benchmark problems and benchmarking rules. Technical Report 21/94 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: Computer ScienceComputer Science (R0)