Multiplicative state-space models for intermittent time series

Ivan Svetunkov and John Edward Boylan

MPRA Paper from University Library of Munich, Germany

Abstract: Intermittent demand forecasting is an important supply chain task, which is commonly done using methods based on exponential smoothing. These methods however do not have underlying statistical models, which limits their generalisation. In this paper we propose a general state-space model that takes intermittence of data into account, extending the taxonomy of exponential smoothing models. We show that this model has a connection with conventional non-intermittent state space models and underlies Croston’s and Teunter-Syntetos-Babai (TSB) forecasting methods. We discuss properties of the proposed models and show how a selection can be made between them in the proposed framework. We then conduct experiments on simulated data and on two real life datasets, demonstrating advantages of the proposed approach.

Keywords: Intermittent demand; supply chain; forecasting; state-space models (search for similar items in EconPapers)
JEL-codes: C53 (search for similar items in EconPapers)
Date: 2017-11-07
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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