Sequential Network Construction for Time Series Prediction

This paper introduces an application of the Sequential Network Construction ( snc) method to select the size of several popular neural network predictor architectures for various benchmark training sets. The specific architectures considered are a fir network and the partially recurrent Elman network and its extension, with context units also added for the output layer. We consider an enhancement of a fir network in which only those weights having relevant time delays are utilized. Bias-variance trade-off in relation to the prediction risk estimation by means of Nonlinear Cross Validation ( ncv) is discussed. The presented approach is applied to the Wölfer sunspot number data and a Mackey-Glass chaotic time series. Results show that the best predictions for the Wölfer data are computed using a fir neural network while for Mackey-Glass data an Elman network yields superior results.