State-space ARIMA for supply-chain forecasting

2019/04/04 by Ivan Svetunkov, John E. Boylan · 1 citation
Decision Sciences · Engineering · #Forecasting Techniques and Applications #Stock Market Forecasting Methods #Energy Load and Power Forecasting

paper · doi:10.1080/00207543.2019.1600764

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.

Citations

• Old dog, new tricks: a modelling view of simple moving averages

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