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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

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>2018
<mark>Journal</mark>International Journal of Production Research
Issue number18
Volume56
Number of pages14
Pages (from-to)6034-6047
Publication statusPublished
Early online date29/09/17
Original languageEnglish

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.

Bibliographic 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