Algorithm for Predicting [UTC–UTC(k)] by Means of Neural Networks

Abstract

This paper presents a new algorithm for predicting the deviations for local timescale UTC(k) in relation to the coordinated universal time (UTC) scale by means of group method of data handling (GMDH) neural network (NN). A very important element of this algorithm significantly affecting the quality of the obtained prediction is the block of time series preparation for NN. On the basis of carried out research for NN training and predicting the deviations, three time series (TS1, TS3, and TS4) are used. The values of elements of first time series (TS1) are calculated based on data from a cesium atomic clock realizing the UTC(k) timescale and on the deviations for this timescale determined by the French for Bureau International des Poids et Mesures (BIPM) in relation to the UTC timescale. The new time series (TS3) is a complement of time series TS1 by the deviations of the UTC(k) timescale in relation to the UTC Rapid timescale. The values of elements of next new time series (TS4) are equal to the deviations for UTC(k) in relation to the UTC and UTC Rapid timescales published by the BIPM. Simulation research of the algorithm has been carried out on the example of the UTC(PL) scale. The best results of [UTC–UTC(PL)] deviation predictions, compared with the previously used method of linear regression, designated with the uncertainty of 8 ns are obtained for GMDH NN and time series TS3.