The Empirical Evidence Steve Morlidge presents results from two test datasets (the first with high levels of manual intervention, the second with intermittent demand patterns), intended to challenge the robustness of the avoidability principle. The first dataset contained one year of weekly forecasts for 124 product SKUs at a fast-moving consumer
Suppose we have a perfect forecasting algorithm. This means that we know the "rule" guiding the behavior we are forecasting (i.e., we know the signal), and we have properly expressed the rule in our forecasting algorithm. As long as the rule governing the behavior doesn't change in the future, then any
While I've long advocated the use of Coefficient of Variation (CV) as a quick and dirty indicator of the forecastability of a time-series, its deficiencies are well recognized. It is true that any series with extremely low CV can be forecast quite accurately (using a moving average or simple exponential smoothing
