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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

Research output: Contribution to journalJournal article

E-pub ahead of print
<mark>Journal publication date</mark>4/04/2019
<mark>Journal</mark>International Journal of Production Research
Publication statusE-pub ahead of print
Early online date4/04/19
Original languageEnglish

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.

Bibliographic 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