  Intermittent demand forecasting is a ubiquitous and challenging problem in
production systems and supply chain management. In recent years, there has been
a growing focus on developing forecasting approaches for intermittent demand
from academic and practical perspectives. However, limited attention has been
given to forecast combination methods, which have achieved competitive
performance in forecasting fast-moving time series. The current study aims to
examine the empirical outcomes of some existing forecast combination methods
and propose a generalized feature-based framework for intermittent demand
forecasting. The proposed framework has been shown to improve the accuracy of
point and quantile forecasts based on two real data sets. Further, some
analysis of features, forecasting pools and computational efficiency is also
provided. The findings indicate the intelligibility and flexibility of the
proposed approach in intermittent demand forecasting and offer insights
regarding inventory decisions.
