A Representation Theory for Morphological Image and Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
Why mathematical morphology needs complete lattices
Signal Processing
Time series and dependent variables
Physica D
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Handbook of Computer Vision Algorithms in Image Algebra
Handbook of Computer Vision Algorithms in Image Algebra
Neural Networks Approach to the Random Walk Dilemma of Financial Time Series
Applied Intelligence
An Introduction to Morphological Neural Networks
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Nonlinear systems and neural networks with hybrid morphological/rank/linear nodes: optimal design and applications to image processing and pattern recognition
Neural Computing and Applications
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Morphological/rank neural networks and their adaptive optimal design for image processing
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Learning to Transform Time Series with a Few Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding the embedding dimension and variable dependencies in time series
Neural Computation
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
Discriminative Learning for Dynamic State Prediction
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to morphological perceptrons with competitive learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A Hybrid Intelligent Morphological Approach for Stock Market Forecasting
Neural Processing Letters
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Lattice algebra approach to single-neuron computation
IEEE Transactions on Neural Networks
Multifeedback-Layer Neural Network
IEEE Transactions on Neural Networks
Support Vector Echo-State Machine for Chaotic Time-Series Prediction
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
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Several linear and non-linear techniques have been proposed to solve the stock market forecasting problem. However, a limitation arises from all these techniques and is known as the random walk dilemma (RWD). In this scenario, forecasts generated by arbitrary models have a characteristic one step ahead delay with respect to the time series values, so that, there is a time phase distortion in stock market phenomena reconstruction. In this paper, we propose a suitable model inspired by concepts in mathematical morphology (MM) and lattice theory (LT). This model is generically called the increasing morphological perceptron (IMP). Also, we present a gradient steepest descent method to design the proposed IMP based on ideas from the back-propagation (BP) algorithm and using a systematic approach to overcome the problem of non-differentiability of morphological operations. Into the learning process we have included a procedure to overcome the RWD, which is an automatic correction step that is geared toward eliminating time phase distortions that occur in stock market phenomena. Furthermore, an experimental analysis is conducted with the IMP using four complex non-linear problems of time series forecasting from the Brazilian stock market. Additionally, two natural phenomena time series are used to assess forecasting performance of the proposed IMP with other non financial time series. At the end, the obtained results are discussed and compared to results found using models recently proposed in the literature.