A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia
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...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
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Academic Press
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/36279 |
| _version_ | 1848754724255301632 |
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| author | Aboagye-Sarfo, P. Mai, Q. Sanfilippo, F. Preen, D. Stewart, Louise Fatovich, D. |
| author_facet | Aboagye-Sarfo, P. Mai, Q. Sanfilippo, F. Preen, D. Stewart, Louise Fatovich, D. |
| author_sort | Aboagye-Sarfo, P. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:44:57Z |
| format | Journal Article |
| id | curtin-20.500.11937-36279 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:44:57Z |
| publishDate | 2015 |
| publisher | Academic Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-362792017-09-13T15:56:36Z A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia Aboagye-Sarfo, P. Mai, Q. Sanfilippo, F. Preen, D. Stewart, Louise Fatovich, D. VARMA models Emergency department demand ARMA models Modelling and forecasting medical services Winters’ method Time series analysis 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. 2015 Journal Article http://hdl.handle.net/20.500.11937/36279 10.1016/j.jbi.2015.06.022 Academic Press fulltext |
| spellingShingle | VARMA models Emergency department demand ARMA models Modelling and forecasting medical services Winters’ method Time series analysis Aboagye-Sarfo, P. Mai, Q. Sanfilippo, F. Preen, D. Stewart, Louise Fatovich, D. A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia |
| title | A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia |
| title_full | A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia |
| title_fullStr | A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia |
| title_full_unstemmed | A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia |
| title_short | A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia |
| title_sort | comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in western australia |
| topic | VARMA models Emergency department demand ARMA models Modelling and forecasting medical services Winters’ method Time series analysis |
| url | http://hdl.handle.net/20.500.11937/36279 |