## 1 Introduction
NNs are very suitable tools used to solve problems of a nonlinear nature [7]. The application of the NN for predicting deviations requires appropriate preparation of input data [8]. In the first half of 2011, paper [9] presents the results of research on the possibility of innovative application of MLP, RBF and GMDH-type NNs for predicting UTC(k). According to the authors' knowledge, it is the first publication in the world presenting the initial results of research on the possibility of applying NNs for predicting deviations for UTC(k). These studies show that a GMDH-type NN increases the accuracy and simplifies the process of predicting deviations compared to the MLP- and RBF-type NNs and the linear regression method used so far by GUM. In subsequent publications of the authors, including [10; 11; 12; 13; 14], the results of work on improving the process of data preparation and UTC(k) prediction by means of the NN have been presented. For several years, a few articles have been published on the application of NNs for predicting deviations for time scales in relation to the UTC scale. The paper [15] presents the first results of research on the application of a one-dimensional convolutional neural network (1D-CNN) for predicting the Japanese UTC(NMI) timescale. They also indicate an improvement in the quality of prediction in relation to the method based on the Kalman filter, which is still used there. On the other hand, work [16] presents the results of research on an integrated model consisting of a gray GM (1,1) and NN model for UT1-UTC prediction. These predictions are essential for space navigation applications and for the precise determination of the orbits of artificial Earth satellites. The papers published in recent years with the application of NNs for predicting UTC(k) have been the motivation for the development of the article, the aim of which is to present the results of the authors' work on the methods of constructing time series for NNs, increasing the accuracy of predicting the deviations for UTC(k). The order of introducing the new developed time series is closely related to the use of the UTC scale, and from 2012 to the UTC and UTC Rapid scales [17]. Chapter 2 of the article provides a short characteristic of the GMDH-type NN and the rationale for its selection for predicting the deviations for UTC(k) scale. Chapter 3 presents the construction of five UTC-based time series. The method of constructing these time series has been assessed on the example of predicting the UTC(PL) scale based on the GMDH-type NN. The next chapter presents the construction of two time series based on the UTC and UTC Rapid scales. The method of constructing these time series has been assessed in the same way as in Chapter 3. Based on the statistical analysis of the obtained prediction results, it is indicated that the TS4_1 time series is the best for predicting deviations for UTC(k). However, in the event of failure or replacement of the atomic clock, the TS5_1 time series should be used. Both time series are based on the UTC and UTC Rapid scales. Since October 2016, they have been used to predict deviations for UTC(PL) implemented by the cesium atomic clock, and since June 2018 by the hydrogen maser.