A Representation Theory for Morphological Image and Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Minimal representations for translation-invariant set mappings by mathematical morphology
SIAM Journal on Applied Mathematics
Adaptive rank order based filters
Signal Processing
Morphology neural networks: an introduction with applications
Circuits, Systems, and Signal Processing - Special issue: networks for neural processing
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Neural Networks Approach to the Random Walk Dilemma of Financial Time Series
Applied Intelligence
Neural Computing and Applications
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
Structuring element adaptation for morphological filters
Journal of Visual Communication and Image Representation
MRL-filters: a general class of nonlinear systems and their optimal design for image processing
IEEE Transactions on Image Processing
Combination of artificial neural-network forecasters for prediction of natural gas consumption
IEEE Transactions on Neural Networks
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Quarterly Time-Series Forecasting With Neural Networks
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
Proceedings of the 2010 ACM Symposium on Applied Computing
A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning
IEEE Transactions on Neural Networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A shift-invariant morphological system for software development cost estimation
Expert Systems with Applications: An International Journal
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
An evolutionary approach to design dilation-erosion perceptrons for stock market indices forecasting
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A robust automatic phase-adjustment method for financial forecasting
Knowledge-Based Systems
Hybrid morphological methodology for software development cost estimation
Expert Systems with Applications: An International Journal
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A dilation-erosion-linear perceptron for bovespa index prediction
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
A hybrid model for s&p500 index forecasting
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting
Neural Processing Letters
Hi-index | 0.02 |
In this paper the morphological-rank-linear time-lag added evolutionary forecasting (MRLTAEF) method is proposed in order to overcome the random walk dilemma for financial time series prediction. It consists of an intelligent hybrid model composed of a morphological-rank-linear (MRL) filter combined with a modified genetic algorithm (MGA), which searches for the particular time lags capable of a fine tuned characterization of the time series and for the estimation of the initial (sub-optimal) parameters of the MRL filter. Each individual of the MGA population is trained by the averaged least mean squares (LMS) algorithm to further improve the parameters of the MRL filter supplied by the MGA. Initially, the proposed MRLTAEF method chooses the most tuned predictive model for time series representation, and then performs a behavioral statistical test in the attempt to adjust time phase distortions that appear in financial time series. Experiments are conducted with the proposed MRLTAEF method using six real-world financial time series according to a group of relevant performance metrics and the results are compared to multilayer perceptron (MLP) networks, MRL filters and the previously introduced time-delay added evolutionary forecasting (TAEF) method.