Forecasting intermittent demand: Traditional smoothing approaches versus the Croston method
Most data scientists use traditional methods of forecasting, such as exponential smoothing or ARIMA, to forecast a product demand. However, when the product experiences several periods of zero demand, approaches such as Croston may provide a better accuracy over these traditional methods. Prateek Nagaria compares traditional and Croston methods in R on intermittent demand time series.
| Talk Title | Forecasting intermittent demand: Traditional smoothing approaches versus the Croston method |
| Speakers | Prateek Nagaria (The Data Team) |
| Conference | Strata + Hadoop World |
| Conf Tag | Make Data Work |
| Location | Singapore |
| Date | December 6-8, 2016 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Intermittent demand—when a product or SKU experiences several periods of zero demand—is highly variable. Intermittent demand is very common in industries such as aviation, automotive, defense, manufacturing, and retail. It also typically occurs with products nearing the end of their lifecycle. However, due to the many zero values in intermittent demand time series, the usual methods of forecasting, such as exponential smoothing and ARIMA, do not give an accurate forecast. In these cases, approaches such as Croston may provide a better accuracy over traditional methods. Prateek Nagaria compares traditional and Croston methods in R on intermittent demand time series. Topics include:
