The article presents results of the influence of the GMDH (Group Method of Data Handling) neural network input data preparation method on the results of predicting corrections for the Polish timescale UTC(PL). Prediction of corrections was carried out using two methods, time series analysis and regression. As appropriate to these methods, the input data was prepared based on two time series, ts1 and ts2. The implemented research concerned the designation of the prediction errors on certain days of the forecast and the influence of the quantity of data on the prediction error. The obtained results indicate that in the case of the GMDH neural network the best quality of forecasting for UTC(PL) can be obtained using the time-series analysis method. The prediction errors obtained did not exceed the value of ± 8 ns, which confirms the possibility of maintaining the Polish timescale at a high level of compliance with the UTC.

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.

Ensuring the best possible stability of UTC(k) (local time scale) and its compliance with the UTC scale (Universal Coordinated Time) forces predicting the [UTC-UTC(k)] deviations, the article presents the results of work on two methods of constructing time series (TS) for a neural network (NN), increasing the accuracy of UTC(k) prediction. In the first method, two prepared TSs are based on the deviations determined according to the UTC scale with a 5-day interval. In order to improve the accuracy of predicting the deviations, the PCHIP interpolating function is used in subsequent TSs, obtaining TS elements with a 1-day interval. A limitation in the improvement of prediction accuracy for these TS has been a too large prediction horizon. The introduction in 2012 of the additional UTC Rapid scale by BIPM makes it possible to shorten the prediction horizon, and the building of two TSs has been proposed according to the second method. Each of them consists of two subsets. The first subset is based on deviations determined according to the UTC scale, the second on the UTC Rapid scale. The research of the proposed TS in the field of predicting deviations for the Polish Timescale by means of GMDH-type NN shows that the best accuracy of predicting the deviations has been achieved for TS built according to the second method.

In this paper, the effectiveness of using Artificial Neural Networks (ANNs) for predicting the corrections of the Polish time scale UTC(PL) (Universal Coordinated Time) is presented. In particular, prediction results for the different types of neural networks, i.e., the MLP (MultiLayer Perceprton), the RBF (Radial Basis Function) and the GMDH (Group Method of Data Handling) are shown. The main advantages and disadvantages of using such types of neural networks are discussed. The prediction of corrections is performed using two methods: the time series analysis method and the regression method. The input data were prepared suitable for the above mentioned methods, based on two time series, ts1 and ts2. The designation of prediction errors for specified days and the influence of data quantity for the prediction error are considered. The paper consists of five sections. After Introduction, in Sec. 2, the theoretical background for different types of neural networks is presented. Section 3 shows data preparation for the appropriate type of neural network. The experimental results are presented in Sec. 4. Finally, Sec. 5 concludes the paper.

The article presents and summarizes the research results of the UTC-UTC(PL) differences predicting procedure for the Polish Time Scale UTC(PL) developed at the Institute of Metrology, Electronics and Computer Science of the University of Zielona Góra. The research has been conducted in close cooperation with the Time and Frequency Laboratory of Central Office of Measures, responsible for the realization of the UTC(PL). The planned experiment covered a 50-month period, in which the UTC(PL) timescale has been carried out using, as a master clock, atomic clocks with different properties: commercial cesium atomic clock 5071A Opt. 001 (first case) and VCH-1003M hydrogen maser (second and third cases). In case of the developed predicting procedure by means of group method of data handling (GMDH)-type neural network (NN), the transitional period (second case) in which the master clock had been replaced by the new clock, is also very important. For GMDH-type NN, input data as a TS1 and TS2 time series have been prepared, based on the already-known differences of UTC(PL) from UTC and UTC rapid timescales and on determined differences between UTC(PL) and the master clock steering the UTC(PL). The method of preparing TS1 and TS2 time series is an important part of the developed procedure for predicting UTC(PL) differences from UTC, before they will be calculated and published by fr. Bureau International des Poids et Mesures (BIPM). The obtained research results confirmed that the developed predicting procedure by means of GMDH-type NN ensures a proper quality of maintaining the national timescale. The best quality of UTC(PL) predicting has been achieved for the third case, both for data prepared according to the TS1 and TS2 time series. This is confirmed by the calculated values of the prediction quality measures and the Modified Allan deviation. On the other hand, the second case confirmed the possibility of using the proprietary development of the TS2 time series to predict UTC-UTC(k) differences. This time series is a very good alternative for the TS1 time series, when the amount of data in this series is insufficient due to replacement of the clock or its failure.

The article presents research results on predicting the deviations for UTC(AOS) timescale realized on the basis of hydrogen maser by means of GMDH type neural network. Input data are prepared in the form of two time series (TS1 and TS2). Time series TS1 is built on the basis of values of comparison between the UTC(AOS) timescale with a clock realizing this scale, and values of deviations determined according to UTC and UTC Rapid scales, published by the BIPM. Time series TS2 is built only on the basis of values of deviations determined according to UTC and UTC Rapid scales. Better quality of predicting the UTC(AOS) timescale is obtained for data prepared in the form of time series TS2. Obtained values of predictions differ from the deviations published by the BIPM at the same day of prediction by ±10 ns for time series TS1 and ±5 ns for time series TS2.