Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
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
Evolving neural networks through augmenting topologies
Evolutionary Computation
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Neural Computing and Applications
Market Index Prediction using Fuzzy Boolean Nets
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Finding the embedding dimension and variable dependencies in time series
Neural Computation
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
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Tuning the structure and parameters of a neural network by using hybrid Taguchi-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
Combining artificial neural network and particle swarm system for time series forecasting
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A prime step in the time series forecasting with hybrid methods: the function choice
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Financial time series prediction using exogenous series and combined neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Proceedings of the 2010 ACM Symposium on Applied Computing
A Hybrid Intelligent Morphological Approach for Stock Market Forecasting
Neural Processing Letters
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
Time series forecasting using a perturbative intelligent system
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A class of hybrid morphological perceptrons with application in time series forecasting
Knowledge-Based Systems
A robust automatic phase-adjustment method for financial forecasting
Knowledge-Based Systems
Concurrency and Computation: Practice & Experience
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
A Morphological-Rank-Linear evolutionary method for stock market prediction
Information Sciences: an International Journal
Evolutionary Learning Processes to Design the Dilation-Erosion Perceptron for Weather Forecasting
Neural Processing Letters
A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading
Computational Economics
Correcting and combining time series forecasters
Neural Networks
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The Time-delay Added Evolutionary Forecasting (TAEF) approach is a new method for time series prediction that performs an evolutionary search for the minimum number of dimensions necessary to represent the underlying information that generates the time series. The methodology proposed is inspired in Takens theorem and consists of an intelligent hybrid model composed of an artificial neural network combined with a modified genetic algorithm. Initially, the TAEF method finds the best fitted model to forecast the series and then performs a behavioral statistical test in order to adjust time phase distortions that may appear in the representation of some series. An experimental investigation conducted with relevant time series show the robustness of the method through a comparison, according to several performance measures, to previous results found in the literature and those obtained with more traditional methods.