  We present a set of models relevant for predicting various aspects of
intra-day trading volume for equities and showcase them as an ensemble that
projects volume in unison. We introduce econometric methods for predicting
total and remaining daily volume, intra-day volume profile (u-curve), close
auction volume and special day seasonalities and emphasize a need for a unified
approach where all sub-models work consistently with one another. Historical
and current inputs are combined using Bayesian methods, which have the
advantage of providing adaptive and parameterless estimations of volume for a
broad range of equities while automatically taking into account uncertainty of
the model input components. The shortcomings of traditional statistical error
metrics for calibrating volume prediction are also discussed and we introduce
Asymmetrical Logarithmic Error (ALE) to overweight an overestimation risk.
