A New Neural Network for Nonlinear Time-Series Modeling

Citation

Hussain A, Soraghan JJ & Durrani T (1997) A New Neural Network for Nonlinear Time-Series Modeling. Journal of Computational Intelligence in Finance, 5 (1), pp. 16-26. http://aiinfinance.com/JCIFIndex.pdf

Abstract
This paper describes a new two-layer linear-in-the-parameters feedforward network termed the Functionally Expanded Neural Network (FENN). The new structure can be considered to be a hybrid neural network incorporating to a variable extent the combined modeling capabilities of the conventional Multi-Layered Perceptron (MLP), Radial Basis Function (RBF) and Volterra Neural Networks (VNN) structures. Simulated chaotic Mackey-Glass time series and real-world noisy, highly non-stationary sunspot and actual stock market time series data are used to illustrate the superior modeling and prediction performance of the FENN compared with other recently reported, more complex feedforward and recurrent neural network based predictor models.

Journal
Journal of Computational Intelligence in Finance: Volume 5, Issue 1