Neural networks for periodicity analysis of unevenly spaced data

Periodicity analysis of unevenly collected data is a relevant issue in several scientific fields. In astrophysics, for example, we have to find the fundamental period of light or radial velocity curves which are unevenly sampled observations of stars. Classical spectral analysis methods are unsatisfactory to face the problem. In this paper we present a neural network based estimator system which performs well the frequency extraction in unevenly sampled signals. It uses a unsupervised Hebbian nonlinear neural algorithm to extract, from the interpolated signal, the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies. The neural network is tolerant to noise amplification due to interpolation and, above all, to blank time window in the data. We benchmark the system on synthetic, realistic and real signals with the Periodogram