TY - UNPB

T1 - Multiplicative State-Space Models for Intermittent Time Series

AU - Svetunkov, Ivan

AU - Boylan, John Edward

PY - 2017/11/7

Y1 - 2017/11/7

N2 - 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.

AB - 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.

KW - Intermittent Demand

KW - Forecasting

KW - Supply Chain

KW - Statistical models

M3 - Working paper

BT - Multiplicative State-Space Models for Intermittent Time Series

PB - Lancaster University Management School

CY - Lancaster

ER -