Prediction of physical realizations of the coordinated universal time with gated recurrent unit

Coordinated Universal Time (UTC), produced by the Bureau International des Poids et Mesures (BIPM), is the official worldwide time reference. Given that there is no physical signal associated with UTC, physical realizations of the UTC, called UTC(k), are very important for demanding applications such as global navigation satellite systems, communication networks, and national defense and security, among others. Therefore, the prediction of the time differences UTC-UTC(k) is important to maintain the accuracy and stability of the UTC(k) timescales. In this paper, we report for the first time the use of a deep learning (DL) technique called Gated Recurrent Unit (GRU) to predict a sequence of H futures values of the time differences UTC-UTC(k) for ten different UTC(k) timescales. UTC-UTC(k) time differences published on the monthly Circular T document of the BIPM are used as training samples. We utilize a multiple-input, multiple-output prediction strategy. After a training process where about 300 past values of the difference UTC-UTC(k) are used, H (H = 6) values of the Circular T can be predicted using p (typically p = 6) past values. The model has been tested with data from ten different UTC(k) timescales. When comparing GRU results with other standard DL algorithms, we found that the GRU approximation has a good performance in predicting UTC(k) timescales. According to our results, the GRU error in predicting UTC-UTC(k) values is typically 1 ns. The frequency instability of the UTC(k) timescale is the main limitation in reducing the GRU error in the time difference prediction.