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  • 2017-09-dog-tricks-modelling

    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

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Old dog, new tricks: a modelling view of simple moving averages

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Old dog, new tricks : a modelling view of simple moving averages. / Svetunkov, Ivan; Petropoulos, Fotios.

In: International Journal of Production Research, Vol. 56, No. 18, 2018, p. 6034-6047.

Research output: Contribution to journalJournal articlepeer-review

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Svetunkov, Ivan ; Petropoulos, Fotios. / Old dog, new tricks : a modelling view of simple moving averages. In: International Journal of Production Research. 2018 ; Vol. 56, No. 18. pp. 6034-6047.

Bibtex

@article{4ad5edef72064250aa732f83ee8fb25b,
title = "Old dog, new tricks: a modelling view of simple moving averages",
abstract = "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.",
keywords = "Forecasting, Supply Chain, State-space models, Statistical models ",
author = "Ivan Svetunkov and Fotios Petropoulos",
note = "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",
year = "2018",
doi = "10.1080/00207543.2017.1380326",
language = "English",
volume = "56",
pages = "6034--6047",
journal = "International Journal of Production Research",
issn = "0020-7543",
publisher = "Taylor and Francis Ltd.",
number = "18",

}

RIS

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 -