Advances in Time Series Forecasting

Volume: 1

Indexed in: Scopus, EBSCO, Ulrich's Periodicals Directory

Time series analysis is applicable in a variety of disciplines, such as business administration, economics, public finance, engineering, statistics, econometrics, mathematics and actuarial sciences. ...
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Forecasting the Number of Outpatient Visits with Different Activation Functions

Pp. 26-33 (8)

Cagdas Hakan Aladag and Sibel Aladag

Abstract

Forecasting the number of outpatient visits plays important role in strategic decisions for the expert of healthcare administration. In order to manage hospitals effectively, it is needed to forecast the number of outpatient visits accurately. In the literature, there have been some methods proposed to forecast these time series. One of these methods is artificial neural networks approach. Although, artificial neural networks have proved its success in forecasting, there are still some problems with using this method. Determining the elements of this method is an important issue. Activation function is a crucial element of artificial neural networks. Therefore, in this study, we examined different activation functions to obtain more accurate out sample predictions while the number of patients is being forecasted. It is found that using different activation function affects the forecasting accuracy of feed forward neural network models.

Keywords:

Activation function, Artificial neural networks, Forecasting, The number of outpatient visits, Time series.

Affiliation:

Hacettepe University, Faculty of Science, Department of Statistics, 06800, Ankara, Turkey