We propose a new aggregation framework for intermittent demand forecasting.
Inverse ADIDA (iADIDA) reduces the variance of the demand sizes.
iADIDA is expected to be particularly effective for erratic demand.
Forecasting and stock control evaluations are performed on two large data sets.