IDEAS home Printed from https://ideas.repec.org/a/taf/thssxx/v10y2021i4p268-285.html
   My bibliography  Save this article

An empirical investigation of forecasting methods for ambulance calls - a case study

Author

Listed:
  • Mohamed A K Al-Azzani
  • Soheil Davari
  • Tracey Jane England

Abstract

A primary goal of emergency services is to minimise the response times to emergencies whilst managing operational costs. This paper is motivated by real data from the Welsh Ambulance Service which in recent years has been criticised for not meeting its eight-minute response target. In this study, four forecasting approaches (ARIMA, Holt Winters, Multiple Regression and Singular Spectrum Analysis (SSA)) are considered to investigate whether they can provide more accurate predictions to the call volume demand (total and by category) than the current approach on a selection of planning horizons (weekly, monthly and 3-monthly). Each method is applied to a training and test set and root mean square error (RMSE) and mean absolute percentage error (MAPE) error statistics are determined. Results showed that ARIMA is the best forecasting method for weekly and monthly prediction of demand and the long-term demand is best predicted using the SSA method.

Suggested Citation

  • Mohamed A K Al-Azzani & Soheil Davari & Tracey Jane England, 2021. "An empirical investigation of forecasting methods for ambulance calls - a case study," Health Systems, Taylor & Francis Journals, vol. 10(4), pages 268-285, October.
    • Handle: RePEc:taf:thssxx:v:10:y:2021:i:4:p:268-285
    DOI: 10.1080/20476965.2020.1783190
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/20476965.2020.1783190
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/20476965.2020.1783190?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:thssxx:v:10:y:2021:i:4:p:268-285. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/thss .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.