  We propose a finite sample based predictor for estimated linear one
dimensional time series models and compute the associated total forecasting
error. The expression for the error that we present takes into account the
estimation error. Unlike existing solutions in the literature, our formulas
require neither assumptions on the second order stationarity of the sample nor
Monte Carlo simulations for their evaluation. This result is used to prove the
pertinence of a new hybrid scheme that we put forward for the forecast of
linear temporal aggregates. This novel strategy consists of carrying out the
parameter estimation based on disaggregated data and the prediction based on
the corresponding aggregated model and data. We show that in some instances
this scheme has a better performance than the "all-disaggregated" approach
presented as optimal in the literature.
