Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7540992 | Computers & Industrial Engineering | 2018 | 12 Pages |
Abstract
The effects of the proposed approach are empirically explored using a set of representative forecasting methods and a dataset of 229 weekly demand series from a leading household and personal care manufacturer in the UK. Our findings suggest that the proposed approach results in more robust predictions with lower mean forecasting error and biases than base forecasts.
Related Topics
Physical Sciences and Engineering
Engineering
Industrial and Manufacturing Engineering
Authors
Marco A. Villegas, Diego J. Pedregal, Juan R. Trapero,