Fast and frugal time series forecasting

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.