  Over the years, families of forecasting models, such as the exponential
smoothing family and Autoregressive Integrated Moving Average, have expanded to
contain multiple possible forms and forecasting profiles. In this paper, we
question the need to consider such large families of models. We argue that
parsimoniously identifying suitable subsets of models will not decrease the
forecasting accuracy nor will it reduce the ability to estimate the forecast
uncertainty. We propose a framework that balances forecasting performance
versus computational cost, resulting in a set of reduced families of models and
empirically demonstrate this trade-offs. We translate computational benefits to
monetary cost savings and discuss the implications of our results in the
context of large retailers.
