  The active galactic nuclei (AGN) are among the most powerful sources with an
inherent, pronounced and random variation of brightness. The randomness of
their time series is so subtle as to blur the border between aperiodic
fluctuations and noisy oscillations. This poses challenges to analysing of such
time series because neither visual inspection nor pre-exisitng methods can
identify well oscillatory signals in them. Thus, there is a need for an
objective method for periodicity detection. Here we review our a new data
analysis method that combines a two-dimensional correlation (2D) of time series
with the powerful methods of Gaussian processes. To demonstrate the utility of
this technique, we apply it to two example problems which were not exploited
enough: damped rednoised artificial time series mimicking AGN time series and
newly published observed time series of changing look AGN (CL AGN) NGC 3516.
The method successfully detected periodicities in both types of time series.
Identified periodicity of $\sim 4$ yr in NGC 3516 allows us to speculate that
if the thermal instability formed in its accretion disc (AD) on a time scale
resembling detected periodicity then AD radius could be $\sim 0.0024$ pc.
