Neural network tools for stellar light prediction

This paper presents a comparative study of state-of-the-art neurocomputing methods applied to several benchmark time series, including the white dwarf light curve. The goal is to determine which of the predictive models work best for data from natural sources. The emphasis is on using a unified methodology for selection of the best architectures among those used for comparison. The specific architectures considered are a Finite Impulse Response (FIR) network and three types of layered recurrent networks: Jordan, Elman, and extended Elman. An enhancement of a FIR network allowing selection of weights with relevant time delays only is also presented. Our approach is applied to two benchmark prediction problems: the Wolfer sunspot number data and a white dwarf light curve. Results show that the best predictions are obtained using a FIR neural network.