Hybrid intelligent methodology to design translation invariant morphological operators for Brazilian stock market prediction

This paper presents a hybrid intelligent methodology to design increasing translation invariant morphological operators applied to Brazilian stock market prediction (overcoming the random walk dilemma). The proposed Translation Invariant Morphological Robust Automatic phase-Adjustment (TIMRAA) method consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) with a Quantum-Inspired Evolutionary Algorithm (QIEA), which searches for the best time lags to reconstruct the phase space of the time series generator phenomenon and determines the initial (sub-optimal) parameters of the MMNN. Each individual of the QIEA population is further trained by the Back Propagation (BP) algorithm to improve the MMNN parameters supplied by the QIEA. Also, for each prediction model generated, it uses a behavioral statistical test and a phase fix procedure to adjust time phase distortions observed in stock market time series. Furthermore, an experimental analysis is conducted with the proposed method through four Brazilian stock market time series, and the achieved results are discussed and compared to results found with random walk models and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) and Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) methods.

[1]  G. Banon,et al.  Minimal representations for translation-invariant set mappings by mathematical morphology , 1991 .

[2]  T. Mills,et al.  The Econometric Modelling of Financial Time Series. , 1995 .

[3]  李幼升,et al.  Ph , 1989 .

[4]  M. B. Priestley,et al.  Non-linear and non-stationary time series analysis , 1990 .

[5]  Jong-Hwan Kim,et al.  Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..

[6]  Graham Kendall,et al.  Co-Evolving Neural Networks with evolutionary Strategies: A New Application to Divisia Money , 2002, IC-AI.

[7]  Xin Yao,et al.  A constructive algorithm for training cooperative neural network ensembles , 2003, IEEE Trans. Neural Networks.

[8]  Guoqiang Peter Zhang,et al.  Quarterly Time-Series Forecasting With Neural Networks , 2007, IEEE Transactions on Neural Networks.

[9]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[10]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[11]  N. Tanaka,et al.  Estimating the active demension of the dynamics in a time series based on an information criterion , 2001 .

[12]  Lutz Prechelt,et al.  PROBEN 1 - a set of benchmarks and benchmarking rules for neural network training algorithms , 1994 .

[13]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[14]  Michael P. Clements,et al.  Forecasting economic and financial time-series with non-linear models , 2004 .

[15]  G. Matheron.  Random Sets and Integral Geometry , 1976 .

[16]  Garrison W. Greenwood,et al.  Evolving artificial neural network structures: experimental results for biologically-inspired adaptive mutations , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[17]  A. Walden,et al.  Spectral analysis for physical applications : multitaper and conventional univariate techniques , 1996 .

[18]  T. Rao,et al.  An Introduction to Bispectral Analysis and Bilinear Time Series Models , 1984 .

[19]  Tiago Alessandro Espínola Ferreira,et al.  Hybrid differential evolutionary system for financial time series forecasting , 2007, 2007 IEEE Congress on Evolutionary Computation.

[20]  Edward J. Coyle,et al.  Stack filters and the mean absolute error criterion , 1988, IEEE Trans. Acoust. Speech Signal Process..

[21]  L. Marton,et al.  Advances in Electronics and Electron Physics , 1958 .

[22]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[23]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[24]  Petros Maragos,et al.  Nonlinear systems and neural networks with hybrid morphological/rank/linear nodes: optimal design and applications to image processing and pattern recognition , 1997 .

[25]  G. X. Ritter,et al.  Recent Developments in Image Algebra , 1991 .

[26]  Tiago Alessandro Espínola Ferreira,et al.  A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks , 2008, Neural Processing Letters.

[27]  R. Savit,et al.  Time series and dependent variables , 1991 .

[28]  Michael P. Clements,et al.  On the limitations of comparing mean square forecast errors: A reply , 1993 .

[29]  Risto Miikkulainen,et al.  Evolving Neural Networks to Play Go , 2004, Applied Intelligence.

[30]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[31]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[32]  Marie Cottrell,et al.  Neural modeling for time series: A statistical stepwise method for weight elimination , 1995, IEEE Trans. Neural Networks.

[33]  Tiago A. E. Ferreira,et al.  An Evolutionary Morphological Approach for Financial Time Series Forecasting , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[34]  Marko Hocevar,et al.  Prediction of cavitation vortex dynamics in the draft tube of a francis turbine using radial basis neural networks , 2004, Neural Computing & Applications.

[35]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[36]  Risto Miikkulainen,et al.  Evolving Neural Networks through Augmenting Topologies , 2002, Evolutionary Computation.

[37]  Petros Maragos,et al.  MRL-filters: a general class of nonlinear systems and their optimal design for image processing , 1998, IEEE Trans. Image Process..

[38]  H. Minkowski.  Gesammelte abhandlungen von Hermann Minkowski, unter mitwirkung von Andreas Speiser und Hermann Weyl hrsg. von David Hilbert , 2022 .

[39]  Alireza Khotanzad,et al.  Combination of artificial neural-network forecasters for prediction of natural gas consumption , 2000, IEEE Trans. Neural Networks Learn. Syst..

[40]  Jennifer L. Davidson,et al.  Morphology neural networks: An introduction with applications , 1993 .

[41]  M. Matilla-García *,et al.  A hybrid approach based on neural networks and genetic algorithms to the study of profitability in the Spanish Stock Market , 2005 .

[42]  Tiago Alessandro Espínola Ferreira,et al.  An intelligent hybrid morphological-rank-linear method for financial time series prediction , 2009, Neurocomputing.

[43]  Petros Maragos,et al.  Character recognition using min-max classifiers designed via an LMS algorithm , 1992, Other Conferences.

[44]  Carsten Peterson,et al.  Finding the Embedding Dimension and Variable Dependencies in Time Series , 1994, Neural Computation.

[45]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[46]  Harvard Univer.  A Representation Theory for Morphological Image and Signal Processing , 1989 .

[47]  M. Pacheco,et al.  Quantum-Inspired Evolutionary Algorithm for Numerical Optimization , 2006 .

[48]  Arie Preminger,et al.  Forecasting Exchange Rates: A Robust Regression Approach , 2005 .

[49]  T. Myhre.  Financial Forecasting at Martin Marietta Energy Systems, Inc , 1992 .

[50]  Stephen S. Wilson.  Morphological Networks , 1989, Other Conferences.

[51]  Edward R. Dougherty,et al.  Facilitation of optimal binary morphological filter design via structuring element libraries and design constraints , 1992 .

[52]  David M. Auslander,et al.  Control and dynamic systems , 1970 .

[53]  Renate Sitte,et al.  Neural Networks Approach to the Random Walk Dilemma of Financial Time Series , 2002, Applied Intelligence.