BaHaMAs: a method for uncertainty quantification in geodetic time series and its application in short-term prediction of length of day
Mostafa Kiani Shahvandi,
Siddhartha Mishra,
Benedikt Soja
Abstract:Some of the important geodetic time series used in various Earth science disciplines are provided without uncertainty estimates. This can affect the validity of conclusions based on such data. However, an efficient uncertainty quantification algorithm to tackle this problem is currently not available. Here we present a methodology to approximate the aleatoric uncertainty in time series, called Bayesian Hamiltonian Monte Carlo Autoencoders (BaHaMAs). BaHaMAs is based on three elements: (1) self-supervised autoe… Show more
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