Timescale Ensemble Performance Enhancement through use of Artificial Neural Network Shikha Maharana ISRO Telemetry Tracking and Command Network Indian Space Research Organization Plot 12 &13, 3 rd Main,2 nd Phase, Peenya Bangalore, 560058 shikha_maharana@istrac.org Aakanksha Avnish Bhardwajan ISRO Telemetry Tracking and Command Network Indian Space Research Organization Plot 12 &13, 3 rd Main,2 nd Phase, Peenya Bangalore, 560058 aakanksha_bhardwajan@istrac.org T Subramanya Ganesh ISRO Telemetry Tracking and Command Network Indian Space Research Organization Plot 12 &13, 3 rd Main,2 nd Phase, Peenya Bangalore, 560058 ganesht@istrac.org Ramakrishna B N ISRO Telemetry Tracking and Command Network Indian Space Research Organization Plot 12 &13, 3 rd Main,2 nd Phase, Peenya Bangalore, 560058 ramki@istrac.org Copyright © 2019 by ISTRAC/ISRO. Permission granted to INCOSE to publish and use. Abstract. A timescale is the estimate of phase and frequency of the “perfect” clock derived from the phase and frequency of the clocks which participate in the ensemble. An ensemble of clocks is the first step towards the realization of a timescale. In the past, several methodologies have been de- veloped to solve the timescale problem such as KAS-2, Multi-scale Ensemble Timescale (METS), ALGOS (BIPM), AT1 (NIST) and algorithm for UTC (CRL). An adaptive ensemble algorithm based on Artificial Neural Network (ANN) has been developed which shows improvements in terms of betterment of frequency stability of the ensemble. The Neural Network in the ensemble algorithm learns differently for different sample spaces of the input phase data that is provided. The weights of the participating clocks are derived from their respective Allan deviations. The weights formulated for the ensemble undergo a regressive iteration to obtain the training data set for parameterization of weights. A single-layer perceptron feedback Neural Network that is proposed in this paper, dynam- ically adapts the weights assigned to the participating clocks based on the paper clock performance. This paper dwells on the comparison of performances between the ANN-based algorithms with that of two other algorithms, namely Reduced Kalman filter based algorithm and METS. Results of ANN-based algorithm for different clock combinations are also presented. The behavior of this al- gorithm in the event of measurement data outage has also been presented in the paper. Keywords: Active Hydrogen MASER (AHM), IRNSS Network Timing facility (IRNWT), Artificial Neural Networks (ANN), Allan Deviation (ADEV), Inverse Allan Variance (IAV), Overlapped Allan Variance (OAV), Multi-scale Ensemble Timescale (METS)