  We apply machine learning to understand fundamental aspects of holographic
duality, specifically the entropies obtained from the apparent and event
horizon areas. We show that simple features of only the time series of the
pressure anisotropy, namely the values and half-widths of the maxima and
minima, the times these are attained, and the times of the first zeroes can
predict the areas of the apparent and event horizons in the dual bulk geometry
at all times with a fixed maximum length (30) of the input vector. Given that
simple Vaidya-type metrics constructed just from the apparent and event horizon
areas can be used to approximately obtain unequal time correlation functions,
we argue that the corresponding entropy functions are the measures of
information that need to be extracted from simple one-point functions to
reconstruct specific aspects of correlation functions of the dual state with
the best possible approximations.
