Academy & Industry Research Collaboration Center (AIRCC)

Volume 9, Number 18, December 2019

Comparison of Time Series Prediction of Healthcare Emergency Department
Indicators with ARIMA and Prophet


Diego Duarte1 and Julio Faerman2, 1University of Greenwich, UK and 2Universidade Autonoma de Barcelona, Spain


Predicting emergency department (ED) indicators in time series may benefit hospital planning, improving quality of care and optimising resources. It motivates analysis of models that can forecast relevant KPIs (Key Performance Indicators) for identifying future pressure. This paper analyses the Autoregressive Integrated Moving Average (ARIMA) method in comparison to the analysis of Prophet, an autoregressive forecasting model based on Re-current Neural Networks. The dataset analysed is formed by hourly valued hospital indicators, composed by Wait to be Seen Major in ED, Number of Attendances Major in ED, Unallocated Patients in ED with a DTA and Number of Beds Available on Medical Acute Unit. A comparison of predictions models ARIMA and Prophet is the focus. Each model is designed to provide better predictions for different time series characteristics. Measurements of best prediction for each indicator are based in accuracy, reliability bands and indicator meta information


Predicting, Healthcare, ARIMA, Prophet & Time series