The task of state estimation in active distribution systems faces a major challenge due to the integration
of different measurements with multiple reporting rates. As a result, distribution systems are essentially
unobservable in real time, indicating the existence of multiple states that result in identical values for
the available measurements. Certain existing approaches utilize historical data to infer the relationship
between real-time available measurements and the state. Other learning-based methods aim to estimate the
measurements acquired with a delay, generating pseudo-measurements. Our paper presents a methodology
that utilizes the outcome of an underdetermined state estimator of an unobservable network state estimator to
exploit information on the joint probability distribution between real-time available measurements and delayed
ones to generate new physics-informed interpretable features. Through numerical simulations conducted on two
realistic distribution grids of different size with insufficient real-time measurements, the proposed procedure
showcases superior performance compared to existing state forecasting approaches and those relying on
inferred pseudo-measurements
