  The time difference between coordinated universal time (UTC) and a hydrogen
maser, which is a master oscillator for the local realization of UTC at the
National Metrology Institute of Japan (NMIJ), has been predicted by using one
of the deep learning techniques called a one-dimensional convolutional neural
network (1D-CNN). Regarding the prediction result obtained by the 1D-CNN, we
have observed improvement in the accuracy of prediction compared with that
obtained by the Kalman filter. Although more investigations are required to
conclude that the 1D-CNN can work as a good predictor, the present results
suggest that the computational approach based on the deep learning technique
may become a versatile method for improving the synchronous accuracy of
UTC(NMIJ) relative to UTC.
