Comparative evaluation of genetic algorithm and backpropagation for training neural networks
Information Sciences—Informatics and Computer Science: An International Journal
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Financial Time Series Data Forecasting by Wavelet and TSK Fuzzy Rule Based System
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Multiple-Period Modified Fuzzy Time-Series for Forecasting TAIEX
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
PEITS '08 Proceedings of the 2008 Workshop on Power Electronics and Intelligent Transportation System
Forecasting Stock Price Using a Genetic Fuzzy Neural Network
ICCSIT '08 Proceedings of the 2008 International Conference on Computer Science and Information Technology
PSO-based single multiplicative neuron model for time series prediction
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
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Forecasting TAIFEX based on fuzzy time series and particle swarm optimization
Expert Systems with Applications: An International Journal
Evolving least squares support vector machines for stock market trend mining
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Evolutionary fuzzy neural networks for hybrid financial prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Functional-Link-Based Neurofuzzy Network for Nonlinear System Control
IEEE Transactions on Fuzzy Systems
Hi-index | 0.00 |
This paper proposes a hybrid model, evolutionary functional link neural fuzzy model (EFLNF), to forecast financial time series where the parameters are optimized by two most efficient evolutionary algorithms: (a) genetic algorithm (GA) and (b) particle swarm optimization (PSO). When the periodicity is just one day, PSO produces a better result than that of GA. But the gap in the performance between them increases as periodicity increases. The convergence speed is also better in case of PSO for one week and one month a head prediction. To testify the superiority of the EFLNF, a number of comparative studies have been made. First, functional link artificial neural network (FLANN) and functional link neural fuzzy (FLNF) were combined with back propagation (BP) learning algorithm. The result shows that FLNF performs better than FLANN. Again, FLNF is compared with EFLNF where the latter outperforms the former irrespective of the periodicity or the learning algorithms with which it has been combined. All models are used to predict the most chaotic financial time series data; BSE Sensex and S&P CNX Nifty stock indices one day, one week and one month in advance.