Quality evaluation of the forecasting system for the Polish Timescale by means of the Group Method of Data Handling type neural network

Abstract

The article presents the results aimed at assessing the effectiveness of the applied forecasting system based on the forecasting procedure using a GMDH (Group Method of Data Handling) neural network for the Polish Timescale (UTC(PL)) implemented on the basis of hydrogen maser (HM). The article also aims to show that the application of the forecasting system to implement the UTC(PL) national timescale makes it possible to achieve a quality of this timescale similar to the best of the best timescales. The results of forecasting using the GMDH neural network for UTC(PL) are presented for two prepared time series (TS1 and TS2), which are additionally compared with the UTC - UTC(k) values for this scale. Very good forecasting quality has been achieved for the analysed cases 5 and 6, both for data prepared according to the TS1 and TS2 time series. This is confirmed by the calculated values of forecast quality measures. The obtained research results show that the developed system allows ensuring the accuracy of the UTC(PL) at the level of best timescales. The presented research results will help to convince some of NMIs (National Metrology Institutes), which are not equipped with caesium fountains, to adopt the UTC(k) steering system.