This paper addresses both theoretical and methodological issues related to
the prediction of long-memory models with incomplete data. Estimates and fo
recasts are calculated by means of state space models and the influence of
data gaps on the performance of short and long run predictions is investiga
ted. These techniques are illustrated with a statistical analysis of the mi
nimum water levels of the Nile river, a time series exhibiting strong depen
dency.