  Long range forecasts are the starting point of many decision support systems
that need to draw inference from high-level aggregate patterns on forecasted
values. State of the art time-series forecasting methods are either subject to
concept drift on long-horizon forecasts, or fail to accurately predict coherent
and accurate high-level aggregates.
  In this work, we present a novel probabilistic forecasting method that
produces forecasts that are coherent in terms of base level and predicted
aggregate statistics. We achieve the coherency between predicted base-level and
aggregate statistics using a novel inference method based on KL-divergence that
can be solved efficiently in closed form. We show that our method improves
forecast performance across both base level and unseen aggregates post
inference on real datasets ranging three diverse domains.
(\href{https://github.com/pratham16cse/AggForecaster}{Project URL})
