A Wrapper for Projection Pursuit Learning

Constructive algorithms have shown to be reliable and effective methods for designing artificial neural networks (ANN) with good accuracy and generalization capability, yet with parsimonious network structures. Projection pursuit learning (PPL) has demonstrated great flexibility and effectiveness in performing this task, though presenting some difficulties in the search for appropriate projection directions in input spaces with high dimensionality. Due to the existence of high-dimensional input spaces in the context of time series prediction, mainly under the existence of long-term dependencies in the time series, we propose here a method based on the wrapper methodology to perform variable selection, so that only a subset of highly-informative lags is going to be considered as the regression vector. The yearly sunspot number time series is adopted as a case study and comparative analysis is performed considering alternative approaches in the literature, guiding to competitive results.