Another Look at Forecast Accuracy Metrics for Intermittent Demand

Some traditional measurements of forecast accuracy are unsuitable for intermittent demand data because they can give infinite or undefined values. Rob Hyndman summarizes these forecast accuracy metrics and explains their potential failings. He also introduces a new metric-the mean absolute scaled error (MASE)-which is more appropriate for intermittent-demand data. More generally, he believes that the MASE should become the standard metric for comparing forecast accuracy across multiple time series. Copyright International Institute of Forecasters, 2006