Mean-Based Error Measures for Intermittent Demand Forecasting

Abstract

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 because of infinities, 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 almost no infinities, and with 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.