A methodology is presented for obtaining optimal forecasts with expone
ntial smoothing (ES) techniques when additional information, other tha
n the historical record of a time series, is available. Such informati
on is usually given as linear restrictions on the future values of the
series and may come from: (i) expert judgments, (ii) alternative fore
casting methods or (iii) scenarios to be portrayed. Appropriate usage
of the additional information improves the forecasts' accuracy and pre
cision. Here we provide closed expressions for the restricted forecast
s obtained with the most frequently employed ES methods and emphasize
the potential usefulness of the proposed methodology in practice.