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  • Svetunkov & Boylan (2019) - State space ARIMA for supply-chain forecasting

    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

    Embargo ends: 4/04/20

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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State-space ARIMA for supply-chain forecasting

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State-space ARIMA for supply-chain forecasting. / Svetunkov, Ivan; Boylan, John Edward.

In: International Journal of Production Research, 04.04.2019.

Research output: Contribution to journalJournal article

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Svetunkov, Ivan ; Boylan, John Edward. / State-space ARIMA for supply-chain forecasting. In: International Journal of Production Research. 2019.

Bibtex

@article{6f69bfb44b3248ada6384c7a84b0c191,
title = "State-space ARIMA for supply-chain forecasting",
abstract = "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.",
keywords = "State space models, ARIMA, Forecasting, Supply chain",
author = "Ivan Svetunkov and Boylan, {John Edward}",
note = "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",
year = "2019",
month = "4",
day = "4",
doi = "10.1080/00207543.2019.1600764",
language = "English",
journal = "International Journal of Production Research",
issn = "0020-7543",
publisher = "Taylor and Francis Ltd.",

}

RIS

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 - 2019/4/4

Y1 - 2019/4/4

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

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

ER -