Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
IEEE Transactions on Neural Networks
A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting
Expert Systems with Applications: An International Journal
Development and performance evaluation of FLANN based model for forecasting of stock markets
Expert Systems with Applications: An International Journal
Short-term stock price prediction based on echo state networks
Expert Systems with Applications: An International Journal
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Credit rating by hybrid machine learning techniques
Applied Soft Computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Predicting stock trends through technical analysis and nearest neighbor classification
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
The application of echo state network in stock data mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Expert Systems with Applications: An International Journal
Structural damage detection using fuzzy cognitive maps and Hebbian learning
Applied Soft Computing
A self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting
WSEAS Transactions on Information Science and Applications
Predicting high-tech equipment fabrication cost with a novel evolutionary SVM inference model
Expert Systems with Applications: An International Journal
Intelligent stock trading system based on improved technical analysis and Echo State Network
Expert Systems with Applications: An International Journal
A self-organized neuro-fuzzy system for stock market dynamics modeling and forecasting
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume II
A new class of hybrid models for time series forecasting
Expert Systems with Applications: An International Journal
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
How many reference patterns can improve profitability for real-time trading in futures market?
Expert Systems with Applications: An International Journal
Using a fuzzy association rule mining approach to identify the financial data association
Expert Systems with Applications: An International Journal
Comparison of multilabel classification models to forecast project dispute resolutions
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Trading team composition for the intraday multistock market
Decision Support Systems
A Combined Forecast Method Integrating Contextual Knowledge
International Journal of Knowledge and Systems Science
Stock indices prediction using radial basis function neural network
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Expert Systems with Applications: An International Journal
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This study investigates the effectiveness of a hybrid approach based on the artificial neural networks (ANNs) for time series properties, such as the adaptive time delay neural networks (ATNNs) and the time delay neural networks (TDNNs), with the genetic algorithms (GAs) in detecting temporal patterns for stock market prediction tasks. Since ATNN and TDNN use time-delayed links of the network into a multi-layer feed-forward network, the topology of which grows by on layer at every time step, it has one more estimate of the number of time delays in addition to several control variables of the ANN design. To estimate these many aspects of the ATNN and TDNN design, a general method based on trial and error along with various heuristics or statistical techniques is proposed. However, for the reason that determining the number of time delays or network architectural factors in a stand-alone mode does not guarantee the illuminating improvement of the performance for building the ATNN and TDNN model, we apply GAs to support optimization of the number of time delays and network architectural factors simultaneously for the ATNN and TDNN model. The results show that the accuracy of the integrated approach proposed for this study is higher than that of the standard ATNN, TDNN and the recurrent neural network (RNN).