A Representation Theory for Morphological Image and Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time series and dependent variables
Physica D
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
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
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
Short-term stock price prediction based on echo state networks
Expert Systems with Applications: An International Journal
A neural network with a case based dynamic window for stock trading prediction
Expert Systems with Applications: An International Journal
Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network
Expert Systems with Applications: An International Journal
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
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
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
Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting
Neural Processing Letters
| Hi-index | 12.05 |
This work presents a method, named Translation Invariant Morphological Time-lag Added Evolutionary Forecasting (TIMTAEF), to overcome the random walk (RW) dilemma for stock market prediction, performing an evolutionary search for the minimum dimension in determining the characteristic phase space that generates the financial time series phenomenon. It is inspired on Takens Theorem and consists of an intelligent hybrid model composed of a Modular Morphological Neural Network (MMNN) 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 estimates the initial (sub-optimal) parameters (weights, architecture and number of modules) of the MMNN. Each individual of the MGA population is trained by the Back Propagation (BP) algorithm to further improve the MMNN parameters supplied by the MGA. After adjusting the model, it performs a behavioral statistical test and a phase fix procedure to adjust time phase distortions observed in financial time series. Furthermore, an experimental analysis is conducted with the proposed model using four real world stock market time series. Five well-known performance metrics and an evaluation function are used to assess the performance of the proposed model and the obtained results are compared to classical models presented in literature.