We develop a framework for prediction of multivariate data that follow some known linear constraints, such as the example where some variables are aggregates of others. This is particularly common when forecasting time series (predicting the future), but also arises in other types of prediction. For point prediction, an increasingly popular technique is reconciliation, whereby predictions are made for all series (so-called “base” predictions) and subsequently adjusted to ensure coherence with the constraints.<img src=“http://feeds.feedburner.com/~r/ProfessorRobJHyndman/~4/oMNRIB6mC