  The algorithms available for retail forecasting have increased in complexity.
Newer methods, such as machine learning, are inherently complex. The more
traditional families of forecasting models, such as exponential smoothing and
autoregressive integrated moving averages, have expanded to contain multiple
possible forms and forecasting profiles. We question complexity in forecasting
and the need to consider such large families of models. Our argument is that
parsimoniously identifying suitable subsets of models will not decrease
forecasting accuracy nor will it reduce the ability to estimate forecast
uncertainty. We propose a framework that balances forecasting performance
versus computational cost, resulting in the consideration of only a reduced set
of models. We empirically demonstrate that a reduced set performs well.
Finally, we translate computational benefits to monetary cost savings and
environmental impact and discuss the implications of our results in the context
of large retailers.
