| Title: |
Forecasting Intermittent Demand by Hyperbolic-Exponential Smoothing |
| Authors: |
Prestwich, S. D., Tarim, S. A., Rossi, R., Hnich, B. |
| Source: |
International Journal of Forecasting, Elsevier, 30(4):928-933, 2014 |
| Publication Year: |
2013 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Other Computer Science |
| More Details: |
Croston's method is generally viewed as superior to exponential smoothing when demand is intermittent, but it has the drawbacks of bias and an inability to deal with obsolescence, in which an item's demand ceases altogether. Several variants have been reported, some of which are unbiased on certain types of demand, but only one recent variant addresses the problem of obsolescence. We describe a new hybrid of Croston's method and Bayesian inference called Hyperbolic-Exponential Smoothing, which is unbiased on non-intermittent and stochastic intermittent demand, decays hyperbolically when obsolescence occurs and performs well in experiments.
Comment: Earlier versions of this work were presented at the 25th European Conference on Operations Research, 2012; and at the 54th Annual Conference of the UK Operational Research Society, 2012. A journal version is in preparation |
| Document Type: |
Working Paper |
| DOI: |
10.1016/j.ijforecast.2014.01.006 |
| Access URL: |
http://arxiv.org/abs/1307.6102 |
| Accession Number: |
edsarx.1307.6102 |
| Database: |
arXiv |
