Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 29/09/2017, available online: http://www.tandfonline.com/10.1080/00207543.2017.1380326
Accepted author manuscript, 620 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Old dog, new tricks
T2 - a modelling view of simple moving averages
AU - Svetunkov, Ivan
AU - Petropoulos, Fotios
N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 29/09/2017, available online: http://www.tandfonline.com/10.1080/00207543.2017.1380326
PY - 2018
Y1 - 2018
N2 - Simple moving average (SMA) is a well-known forecasting method. It is easy to understand and interpret and easy to use, but it does not have an appropriate length selection mechanism and does not have an underlying statistical model. In this paper, we show two statistical models underlying SMA and demonstrate that the automatic selection of the optimal length of the model can easily be done using this finding. We then evaluate the proposed model on a real data-set and compare its performance with other popular simple forecasting methods. We find that SMA performs better both in terms of point forecasts and prediction intervals in cases of normal and cumulative values.
AB - Simple moving average (SMA) is a well-known forecasting method. It is easy to understand and interpret and easy to use, but it does not have an appropriate length selection mechanism and does not have an underlying statistical model. In this paper, we show two statistical models underlying SMA and demonstrate that the automatic selection of the optimal length of the model can easily be done using this finding. We then evaluate the proposed model on a real data-set and compare its performance with other popular simple forecasting methods. We find that SMA performs better both in terms of point forecasts and prediction intervals in cases of normal and cumulative values.
KW - Forecasting
KW - Supply Chain
KW - State-space models
KW - Statistical models
U2 - 10.1080/00207543.2017.1380326
DO - 10.1080/00207543.2017.1380326
M3 - Journal article
VL - 56
SP - 6034
EP - 6047
JO - International Journal of Production Research
JF - International Journal of Production Research
SN - 0020-7543
IS - 18
ER -