Forecast combinations for intermittent demand

Petropoulos, Fotios and Kourentzes, Nikos (2015) Forecast combinations for intermittent demand. Journal of the Operational Research Society, 66 (6). pp. 914-924. ISSN 0160-5682

[thumbnail of JORS 2014 (preprint)]
PDF (JORS 2014 (preprint))
JORS_2014_preprint_.pdf - Submitted Version

Download (200kB)


Intermittent demand is characterised by infrequent demand arrivals, where many periods have zero demand, coupled with varied demand sizes. The dual source of variation renders forecasting for intermittent demand a very challenging task. Many researchers have focused on the development of specialised methods for intermittent demand. However, apart from a case study on hierarchical forecasting, the effects of combining, which is a standard practice for regular demand, have not been investigated. This paper empirically explores the efficiency of forecast combinations in the intermittent demand context. We examine both method and temporal combinations of forecasts. The first are based on combinations of different methods on the same time series, while the latter use combinations of forecasts produced on different views of the time series, based on temporal aggregation. Temporal combinations of single or multiple methods are investigated, leading to a new time series classification, which leads to model selection and combination. Results suggest that appropriate combinations lead to improved forecasting performance over single methods, as well as simplifying the forecasting process by limiting the need for manual selection of methods or hyper-parameters of good performing benchmarks. This has direct implications for intermittent demand forecasting in practice.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the Operational Research Society
Additional Information:
This is a pre-print of an article published in Journal of the Operational Research Society. The definitive publisher-authenticated version is available online at:
Uncontrolled Keywords:
?? intermittent demandparametric methodscombiningtemporal aggregationclassificationforecastingmanagement information systemsstrategy and managementmanagement science and operations researchmarketing ??
ID Code:
Deposited By:
Deposited On:
19 May 2014 10:07
Last Modified:
13 Jan 2024 00:12