Submitted manuscript, 485 KB, PDF document
Research output: Working paper
Research output: Working paper
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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 -