Neural computing: an introduction
Neural computing: an introduction
Forecasting exchange rates using general regression neural networks
Computers and Operations Research - Neural networks in business
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Global hybrid ant bee colony algorithm for training artificial neural networks
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part I
Expert Systems with Applications: An International Journal
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
Hi-index | 12.07 |
This paper considers the prediction of noisy time series data, specifically, the prediction of financial signals. A novel Dynamic Ridge Polynomial Neural Network (DRPNN) for financial time series prediction is presented which combines the properties of both higher order and recurrent neural network. In an attempt to overcome the stability and convergence problems in the proposed DRPNN, the stability convergence of DRPNN is derived to ensure that the network posses a unique equilibrium state. In order to provide a more accurate comparative evaluation in terms of profit earning, empirical testing used in this work encompass not only on the more traditional criteria of NMSE, which concerned at how good the forecasts fit their target, but also on financial metrics where the objective is to use the networks predictions to generate profit. Extensive simulations for the prediction of one and five steps ahead of stationary and non-stationary time series were performed. The resulting forecast made by DRPNN shows substantial profits on financial historical signals when compared to various neural networks; the Pi-Sigma Neural Network, the Functional Link Neural Network, the feedforward Ridge Polynomial Neural Network, and the Multilayer Perceptron. Simulation results indicate that DRPNN in most cases demonstrated advantages in capturing chaotic movement in the financial signals with an improvement in the profit return and rapid convergence over other network models.