Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 04/04/2019, available online: https://www.tandfonline.com/doi/full/10.1080/00207543.2019.1600764
Accepted author manuscript, 236 KB, PDF document
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - State-space ARIMA for supply-chain forecasting
AU - Svetunkov, Ivan
AU - Boylan, John Edward
N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 04/04/2019, available online: https://www.tandfonline.com/doi/full/10.1080/00207543.2019.1600764
PY - 2020/2/1
Y1 - 2020/2/1
N2 - ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.
AB - ARIMA is seldom used in supply chains in practice. There are several reasons, not the least of which is the small sample size of available data, which restricts the usage of the model. Keeping in mind this restriction, we discuss in this paper a state-space ARIMA model with a single source of error and show how it can be efficiently used in the supply-chain context, especially in cases when only two seasonal cycles of data are available. We propose a new order selection algorithm for the model and compare its performance with the conventional ARIMA on real data. We show that the proposed model performs well in terms of both accuracy and computational time in comparison with other ARIMA implementations, which makes it efficient in the supply-chain context.
KW - State space models
KW - ARIMA
KW - Forecasting
KW - Supply chain
U2 - 10.1080/00207543.2019.1600764
DO - 10.1080/00207543.2019.1600764
M3 - Journal article
VL - 58
SP - 818
EP - 827
JO - International Journal of Production Research
JF - International Journal of Production Research
SN - 0020-7543
IS - 3
ER -