Neural networks and the bias/variance dilemma
Neural Computation
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Diagnosing shocks in stock markets of southeast Asia, Australia, and New Zealand
Mathematics and Computers in Simulation - Selected papers of the MSSANZ/IMACS 13th biennial conference on modelling and simulation, Hamilton, New Zealand, December 1999
Hybrid Intelligent Systems for Stock Market Analysis
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Efficient Locally Weighted Polynomial Regression Predictions
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Performance analysis of connectionist paradigms for modeling chaotic behavior of stock indices
Second international workshop on Intelligent systems design and application
Modeling chaotic behavior of stock indices using intelligent paradigms
Neural, Parallel & Scientific Computations - Special issue: Advances in intelligent systems and applications
An ensemble of neural networks for weather forecasting
Neural Computing and Applications
Agent-based computational modeling of the stock price-volume relation
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Feature selection and intrusion detection using hybrid flexible neural tree
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Forecasting stock market short-term trends using a neuro-fuzzy based methodology
Expert Systems with Applications: An International Journal
Neural Networks
Using Chaotic Neural Network to Forecast Stock Index
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Inference of Differential Equations for Modeling Chemical Reactions
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Time-series forecasting using a system of ordinary differential equations
Information Sciences: an International Journal
Design of ensemble neural network using entropy theory
Advances in Engineering Software
A parallel evolving algorithm for flexible neural tree
Parallel Computing
Small-time scale network traffic prediction based on flexible neural tree
Applied Soft Computing
Concurrency and Computation: Practice & Experience
Swarm optimization and Flexible Neural Tree for microarray data classification
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Towards independent color space selection for human skin detection
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression
Computers & Mathematics with Applications
Local prediction of network traffic measurements data based on relevance vector machine
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Automated trading with performance weighted random forests and seasonality
Expert Systems with Applications: An International Journal
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The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using flexible neural tree (FNT) ensemble technique. We considered the Nasdaq-100 index of Nasdaq Stock Market^S^M and the S&P CNX NIFTY stock index. We analyzed 7-year Nasdaq-100 main index values and 4-year NIFTY index values. This paper investigates the development of novel reliable and efficient techniques to model the seemingly chaotic behavior of stock markets. The structure and parameters of FNT are optimized using genetic programming (GP) like tree structure-based evolutionary algorithm and particle swarm optimization (PSO) algorithms, respectively. A good ensemble model is formulated by the local weighted polynomial regression (LWPR). This paper investigates whether the proposed method can provide the required level of performance, which is sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experimental results show that the model considered could represent the stock indices behavior very accurately.