Estimation of periodicity in non-uniformly sampled time series data is frequently a goal in astronomical data analysis. There are various problems faced: Firstly, data is sampled non-uniformly which makes it difficult to use simple Fourier transform for performing spectral analysis. Secondly, there are large gaps in data which makes it difficult to interpolate the signal for re-sampling. Thirdly, in data sets with smaller time periods the non-uniformity in sampling and noise in data pose even greater problems because of the lesser number of samples per period. Finally, recent use of CCD technology has enabled collection of vast amounts of data from various sources. In order to process this huge amount of data we also need to remove human intervention from the process of periodicity estimation to make the algorithm more efficient. In the present work we focus on correntropy and design a new spatio-temporal kernel to accurately estimate the time period of the data without any human intervention.

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