Abstract
Real-world financial data is often discontinuous and non-smooth. Neural network group models can perform this function with more accuracy. Both polynomial higher order neural network group (PHONNG) and trigonometric polynomial higher order neural network group (THONNG) models are studied in this chapter. These PHONNG and THONNG models are open box, convergent models capable of approximating any kind of piecewise continuous function, to any degree of accuracy. Moreover, they are capable of handling higher frequency, higher order nonlinear, and discontinuous data. Results confirm that PHONNG and THONNG group models converge without difficulty and are considerably more accurate (0.7542% - 1.0715%) than neural network models such as using polynomial higher order neural network (PHONN) and trigonometric polynomial higher order neural network (THONN) models.
Reference66 articles.
1. Foreign Exchange Rate Forecasting Using Higher Order Flexible Neural Tree
2. Neural Network Models for Prediction of Steady-State and Dynamic Behavior of MAPK Cascade
3. Data Simulation Using SINCHONN Model;J.Crane;Proceedings of IASTED International Conference on Computational Intelligence,2005
4. Draye, J. S., Pavisic, D. A., Cheron, G. A., & Libert, G. A. (1996), Dynamic recurrent neural networks: a dynamic analysis, IEEE Transactions SMC- Part B, 26(5), 692-706.
5. Modelling and Trading the Soybean-Oil Crush Spread with Recurrent and Higher Order Networks: A Comparative Analysis;C. L.Dunis;Neural Network World,2006