Practical neural network recipes in C++
Practical neural network recipes in C++
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
A fusion model of HMM, ANN and GA for stock market forecasting
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Neural Networks
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Evolutional RBFNs prediction systems generation in the applications of financial time series data
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
International Journal of Data Analysis Techniques and Strategies
Information Systems Frontiers
An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting
International Journal of Applied Evolutionary Computation
Functional link neural network: artificial bee colony for time series temperature prediction
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
International Journal of Business Intelligence and Data Mining
A hybrid FLANN and adaptive differential evolution model for forecasting of stock market indices
International Journal of Knowledge-based and Intelligent Engineering Systems
Hi-index | 12.05 |
A trigonometric functional link artificial neural network (FLANN) model for short (one day) as well as long term (one month, two months) prediction of stock price of leading stock market indices: DJIA and S&P 500 is developed in this paper. The proposed FLANN model employs the least mean square (LMS) as well as the recursive least square (RLS) algorithms in different experiments to train the weights of the model. The historical index data transformed into various technical indicators as well as macro economic data as fundamental factors are considered as inputs to the proposed models. The mean absolute percentage error (MAPE) with respect to actual stock prices is selected as the performance index to gauge the quality of prediction of the models. Extensive simulation and test results show that the application of FLANN to the stock market prediction problem gives out results which are comparable to other neural network models. In addition the proposed models are structurally simple and requires less computation during training and testing as the model contains only one neuron and one layer. Between the two models proposed the FLANN-RLS requires substantially less experiments to train compared to the LMS based model. This feature makes the RLS-based FLANN model more suitable for online prediction.