  The extremely low flux of ultra-high energy cosmic rays (UHECR) makes their
direct observation by orbital experiments practically impossible. For this
reason all current and planned UHECR experiments detect cosmic rays indirectly
by observing the extensive air showers (EAS) initiated by cosmic ray particles
in the atmosphere. The world largest statistics of the ultra-high energy EAS
events is recorded by the networks of surface stations. In this paper we
consider a novel approach for reconstruction of the arrival direction of the
primary particle based on the deep convolutional neural network. The latter is
using raw time-resolved signals of the set of the adjacent trigger stations as
an input. The Telescope Array (TA) Surface Detector (SD) is an array of 507
stations, each containing two layers plastic scintillator with an area of $3$
m$^2$. The training of the model is performed with the Monte-Carlo dataset. It
is shown that within the Monte-Carlo simulations, the new approach yields
better resolution than the traditional reconstruction method based on the
fitting of the EAS front. The details of the network architecture and its
optimization for this particular task are discussed.
