A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia

P. Aboagye-Sarfo, Bella Mai, Frank Sanfilippo, David Preen, L.M. Stewart, Daniel Fatovich

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

© 2015 Elsevier Inc.. Objective: To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models. Methods: Seven-year monthly WA state-wide public hospital ED presentation data from 2006/07 to 2012/13 were modelled. Graphical and VARMA modelling methods were used for descriptive analysis and model fitting. The VARMA models were compared to the benchmark univariate ARMA and Winters' models to determine their accuracy to predict ED demand. The best models were evaluated by using error correction methods for accuracy. Results: Descriptive analysis of all the dependent variables showed an increasing pattern of ED use with seasonal trends over time. The VARMA models provided a more precise and accurate forecast with smaller confidence intervals and better measures of accuracy in predicting ED demand in WA than the ARMA and Winters' method. Conclusion: VARMA models are a reliable forecasting method to predict ED demand for strategic planning and resource allocation. While the ARMA models are a closely competing alternative, they under-estimated future ED demand.
Original languageEnglish
Pages (from-to)62-73
JournalJournal of Biomedical Informatics
Volume57
DOIs
Publication statusPublished - 2015

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