A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
Fewer Hyper-Ellipsoids Fuzzy Rules Generation Using Evolutional Learning Scheme
Cybernetics and Systems
A type-2 fuzzy rule-based expert system model for stock price analysis
Expert Systems with Applications: An International Journal
Fuzzy dual-factor time-series for stock index forecasting
Expert Systems with Applications: An International Journal
Using neural networks and data mining techniques for the financial distress prediction model
Expert Systems with Applications: An International Journal
Evolving and clustering fuzzy decision tree for financial time series data 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
A neural network with a case based dynamic window for stock trading prediction
Expert Systems with Applications: An International Journal
Financial distress prediction by a radial basis function network with logit analysis learning
Computers & Mathematics with Applications
Dynamic adaptive ensemble case-based reasoning: application to stock market prediction
Expert Systems with Applications: An International Journal
A hybrid clustering and gradient descent approach for fuzzymodeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improvement of accuracy in a sound synthesis method using Evolutionary Product Unit Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
An artificial neural prediction system is automatically developed with the combinations of step wise regression analysis (SRA), dynamic learning and recursive-based particle swarm optimization (RPSO) learning algorithms. In the first stage, the SRA can be considered like a data filtering machine to choose two primary factors from 20 channel technical indexes as input variables of the RBFNs system. Then, an efficient dynamic learning algorithm is applied to sequentially generate RBFs functions from training data set, where it can efficiently determine the proper number of RBFs' centers and their associated positions. It can be exploited to forecast appropriate behaviors of the wanted identified financial time series data. While characteristics of training data set are automatically mined and generated by the proposed dynamic learning algorithm, architecture of the RBFNs prediction system is initially represented with collected information. Moreover, the RPSO learning scheme with the hybrid particle swarm optimization (PSO) and recursive least-squares (RLS) learning methods are applied to extract those appropriate parameters of the RBFNs prediction system. The RBFNs prediction systems are implemented in data analysis, module generation and price trend of the financial time series data. It not only automatically determines proper RBFs number but also fast approach the desired target in actual trading of Taiwan stock index (TAIEX). Computer simulations in training and testing phases of historic TAIEX are compared with other learning methods, which illustrate our great performance not only increases the accuracy of the stock price prediction but also improves the win rate in the trend of TAIEX.