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

Aboagye-Sarfo, Patrick; Mai, Qun; Sanfilippo, Frank M; Preen, David B.; Stewart, Louise M.; Fatovich, Daniel M

doi:10.1016/j.jbi.2015.06.022

Cited by 97 publications

(53 citation statements)

References 13 publications

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“…The findings of the present study indicate that time series analysis is a better predictive tool for health service demand modelling than the linear rate model. It has proved to be a robust predictive method for overall ET demand and by age group and sex, confirming our previous studies . Our study shows a significant increase in ET demand in WA up to 2020 in all age groups, especially in older people aged ≥65 years.…”

Section: Discussionsupporting

confidence: 91%

Impact of population ageing on growing demand for emergency transportation to emergency departments in Western Australia, 2005–2020

Aboagye-Sarfo

Mai

Sanfilippo

et al. 2016

Emerg Medicine Australasia

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Section: Discussionsupporting

confidence: 91%

Impact of population ageing on growing demand for emergency transportation to emergency departments in Western Australia, 2005–2020

Aboagye-Sarfo

Mai

Sanfilippo

et al. 2016

Emerg Medicine Australasia

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“…Due to the ability of effectively extracting the linear features of randomness and trend, ARIMA model has found extensive applications in forecasting hospital daily visits of ED and OD [6, 19, 20]. Furthermore, aiming to better capture cyclicity over a period of week in daily time series data, ARIMA models have been extended and modified among which seasonal ARIMA (SARIMA) and multiplicative seasonal ARIMA (MSARIMA) models are the most widely used [3, 7, 21].…”

Section: Introductionmentioning

confidence: 99%

Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models

Luo

Zhang

et al. 2017

BMC Health Serv Res

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BackgroundAccurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors’ scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration.MethodsWe formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead.ResultsThe proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better.ConclusionsCombinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.

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“…However, some researchers think that the univariate models performed similarly to the multivariate models [39]. Additional comparisons of the two models can be determined in different fields, such as the prediction of emergency department demand [40] and energy market volatility [41]. …”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China

et al. 2017

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The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.

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

Abstract: 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.

Cited by 97 publications

References 13 publications

Impact of population ageing on growing demand for emergency transportation to emergency departments in Western Australia, 2005–2020

Impact of population ageing on growing demand for emergency transportation to emergency departments in Western Australia, 2005–2020

Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China

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