Theory and Application of James-Stein Seasonal Forecasting Model for Short Time Series

DOI

10.2991/lemcs-14.2014.220How to use a DOI?

Keywords

forcasting; seasonality; short time series; James-Stein; supply chain

Abstract

Accurate seasonal forecasting plays an important role in product demand forecast. This paper gives an insight into the theory of a James-Stein seasonal forecasting model. The conditions in which the method outperforms the classical decomposition method are then presented. The conditions show that James-Stein model has more accurate prediction results when dealing with large noise data. The conclusion is then examined through a set of real data from M-competition. The experimental results confirm the practical value of the theory.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hui Mao
AU  - Kui Zhang
PY  - 2014/05
DA  - 2014/05
TI  - Theory and Application of James-Stein Seasonal Forecasting Model for Short Time Series
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 979
EP  - 982
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-14.2014.220
DO  - 10.2991/lemcs-14.2014.220
ID  - Mao2014/05
ER  -