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Demand forecasting with four-parameter exponential smoothing

05.11.2016Comments are closed.

Keywords:

Demand forecasting,
Exponential smoothing methods,
Seasonal data,
Holt-Winters methods,
Damped trend methods,
M3-Competition,
Individual products,
Symmetric relative efficiency measure

Author(s):

Liljana Ferbar Tratar, PhD, University of Ljubljana Faculty of Economics

Blaž Mojškerc, PhD, University of Ljubljana Faculty of Economics

Aleš Toman, PhD,  University of Ljubljana Faculty of Economics

Abstract:

Exponential smoothing methods are powerful tools for denoising time series, predicting future demand and decreasing inventory costs. In this paper we develop a smoothing and forecasting method that is intuitive, easy to implement, computationally stable, and can satisfactorily handle both, additive and multiplicative seasonality, even when time series contain several zero entries and large noise component.

We start with the classical additive Holt-Winters method and introduce an additional smoothing parameter in the level recurrence equation. All parameters are required to lie within [0,1] and estimated by minimizing the one-step-ahead forecasting errors in the sample. Doing so, the errors decrease substantially, especially for the time series with strong trends. The newly developed method produces more accurate short-term out-of-sample forecasts than the classical Holt-Winters methods and the Holt-Winters methods with damped trend.

The performance of the method is evaluated using a battery of real quarterly and monthly time series from the M3-Competition. A simulation study is conducted for further in-depth analysis of the method under different demand patterns. We developed and justified the use of a symmetric relative efficiency measure that allows researchers ad practitioners to evaluate the performance of different smoothing and forecasting methods.

Journal:

International Journal of Production Economics 181 (2016) 162-173

Indexing:

JCR 2015: IF 2.782

Kategorija SE

IJ – engineering, industrial ; 2/44 ; četrtina: 1

IK – engineering, manufacturing ; 4/42 ; četrtina: 1

PE – operations research & management science ; 7/82; četrtina: 1

 

SNIP 2015: IF 2.109

Kategorija SE

2209 – Industrial and Manufacturing Engineering ; 18/260 ; četrtina: 1

 

Kategorija SSE

1400 – Business, Management and Accounting(all) ; 7/169 ; četrtina: 1

1803 – Management Science and Operations Research ; 11/126 ; četrtina: 1

2002 -Economics and Econometrics ; 41/516 ; četrtina: 1

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