  To compare different forecasting methods on demand series we require an error
measure. Many error measures have been proposed, but when demand is
intermittent some become inapplicable, some give counter-intuitive results, and
there is no agreement on which is best. We argue that almost all known measures
rank forecasters incorrectly on intermittent demand series. We propose several
new error measures with wider applicability, and correct forecaster ranking on
several intermittent demand patterns. We call these "mean-based" error measures
because they evaluate forecasts against the (possibly time-dependent) mean of
the underlying stochastic process instead of point demands.
