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Forecasting is an indispensable element of operational research (OR) and animportant aid to planning. The accurate estimation of the forecast uncertaintyfacilitates several operations management activities, predominantly insupporting decisions in inventory and supply chain management and effectivelysetting safety stocks. In this paper, we introduce a feature-based framework,which links the relationship between time series features and the intervalforecasting performance into providing reliable interval forecasts. We proposean optimal threshold ratio searching algorithm and a new weight determinationmechanism for selecting an appropriate subset of models and assigningcombination weights for each time series tailored to the observed features. Weevaluate our approach using a large set of time series from the M4 competition.Our experiments show that our approach significantly outperforms a wide rangeof benchmark models, both in terms of point forecasts as well as predictionintervals.

The uncertainty estimation of feature-based forecast combinations