## 5 Conclusions  
Predicting the [UTC-UTC(k)] deviations can be carried out by means of analytical or neural networks methods. Regardless of the method used, there is a need to properly prepare time series for prediction. Until now, these time series have been built with an interval of five days, which had a significant impact on the quality of predicting the deviations. This interval is determined by the way that BIPM publishes the [UTC-UTC(k)] deviations for each UTC(k) scale. The article proposes two methods of constructing time series, which have a significant impact on the quality of predicting the [UTC-UTC(k)] deviations. The GMDH-type neural network has been used for predicting the deviations.  
In the first method of constructing time series, based on deviations determined according to the UTC scale, the improvement of the prediction quality has been obtained by building the TS1_1, TS2_1, and TS3_1 time series with a one-day interval based on the PCHIP interpolation function. The limitation of the improvement in the quality of prediction has been the too long prediction horizon, from 10 to 20 days. The best quality of predicting the deviations has been obtained for TS1_1. On the other hand, the TS3_1, despite the lower quality of deviation predicting achieved, can be used in the case of failure of the atomic clock or its replacement.  
The second proposed method of constructing time series is based on the deviations published by BIPM according to the UTC and UTC Rapid scales. Two time series, TS4_1 and TS5_1, have been proposed, each of which consists of two subsets. In the first subset of the data group for both time series, based on the values of \(xb(t)\) deviations determined according to the UTC scale, the PCHIP function has been used to determine the values of these time series for each day. In the second subset of data for TS4_1 and TS5_1, \(xbr(t)\) deviation values determined according to the UTC Rapid scale have been used, which significantly reduced the prediction horizon (from three to nine days). As a result, these activities make it possible to predict the deviations for UTC(PL) with a discrepancy not exceeding \(\pm\)10 ns from the [UTC-UTC(PL)] deviations published by BIPM.  
The presented research results have been the basis for the selection of the method of constructing the TS4_1 series to predict the deviations for UTC(PL) by means of the GMDH-type neural network. They have been carried out since October 2016. In the first period, the UTC(PL) scale was implemented by the cesium atomic clock, and since June 2018 it was implemented by the hydrogen maser. During the transitional period, after replacing the cesium clock with a hydrogen maser, the TS5_1 has been used for predicting the [UTC-UTC(PL)] deviations. For the hydrogen maser, the values of predicted [UTC-UTC(PL)] deviations for both time series do not exceed a few ns.  
Currently, at the IMEI, in cooperation with the GUM, preliminary work is underway on the concept of preparing a new type of time series for predicting the Polish Time Scale UTC(PL).  
The results and conclusions resulting from the presented research on the methods of preparing time series may be useful in predicting UTC(k) with analytical methods and with the use of NNs. The recent years indicate that works on the application of NNs for predicting UTC(k) are being carried out in other centers [15; 16].  
Conceptualization, L.S. and W.M.; methodology, L.S. and W.M.; software, L.S. and W.M.; validation, L.S. and W.M.; formal analysis, L.S. and W.M.; investigation, L.S. and W.M.; resources, L.S. and W.M.; data curation, L.S. and W.M.; writing--original draft preparation, L.S. and W.M.; writing--review and editing, L.S. and W.M.; visualization, L.S. and W.M.; supervision, L.S. and W.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
M. MDPI Research Data Policies.  
The authors declare no conflict of interest.