• Jochen Bergs, Philipe Heerinckx, and Sandra Verelst.
• Research group Economics and Public Policy, Faculty of Business Economics, Hasselt University, Belgium. Electronic address: Jochen.bergs@uhasselt.be.
• Int Emerg Nurs. 2014 Apr 1;22(2):112-5.

ObjectiveTo evaluate an automatic forecasting algorithm in order to predict the number of monthly emergency department (ED) visits one year ahead.MethodsWe collected retrospective data of the number of monthly visiting patients for a 6-year period (2005-2011) from 4 Belgian Hospitals. We used an automated exponential smoothing approach to predict monthly visits during the year 2011 based on the first 5 years of the dataset. Several in- and post-sample forecasting accuracy measures were calculated.ResultsThe automatic forecasting algorithm was able to predict monthly visits with a mean absolute percentage error ranging from 2.64% to 4.8%, indicating an accurate prediction. The mean absolute scaled error ranged from 0.53 to 0.68 indicating that, on average, the forecast was better compared with in-sample one-step forecast from the naïve method.ConclusionThe applied automated exponential smoothing approach provided useful predictions of the number of monthly visits a year in advance.Copyright © 2013 Elsevier Ltd. All rights reserved.

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