{"id":1642,"date":"2020-05-25T13:13:44","date_gmt":"2020-05-25T13:13:44","guid":{"rendered":"http:\/\/kourentzes.com\/forecasting\/?p=1642"},"modified":"2020-06-04T21:54:28","modified_gmt":"2020-06-04T21:54:28","slug":"elucidate-structure-in-intermittent-demand-time-series","status":"publish","type":"post","link":"https:\/\/kourentzes.com\/forecasting\/2020\/05\/25\/elucidate-structure-in-intermittent-demand-time-series\/","title":{"rendered":"Elucidate structure in intermittent demand time series"},"content":{"rendered":"

Nikolaos Kourentzes and George Athanasopoulos, 2020. European Journal of Operational Research. https:\/\/doi.org\/10.1016\/j.ejor.2020.05.046<\/a><\/p>\n

Intermittent demand forecasting has been widely researched in the context of spare parts management. However, it is becoming increasingly relevant to many other areas, such as retailing, where at the very disaggregate level time series may be highly intermittent, but at more aggregate levels are likely to exhibit trends and seasonal patterns. The vast majority of intermittent demand forecasting methods are inappropriate for producing forecasts with such features. We propose using temporal hierarchies to produce forecasts that demonstrate these traits at the various aggregation levels, effectively informing the resulting intermittent forecasts of these patterns that are identifiable only at higher levels. We conduct an empirical evaluation on real data and demonstrate statistically significant gains for both point and quantile forecasts.<\/p>\n

Download paper<\/a>.<\/p>\n