Model Building for Autocorrelated Process Control: An Industrial Experience

Djauhari, Maman A.; Lee, S. L.; Ismail, Zuhaimy

doi:10.3844/ajassp.2014.888.898

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…White noise was tested using the Box-Ljung test (Seiler and Rom, 1997). The Jarque-Bera test (Jarque, 2011) and the Anderson-Darling test for normality (Djauhari et al, 2014) were also used. The auto-correlation function and partial auto-correlation function were then plotted to determine the autoregressive integrated moving average (p, d, q) where p is the order of auto-regression, d is the lagged difference between the current and previous values, and q denotes the order of the moving average.…”

Section: Framework Explaining the Methodsmentioning

confidence: 99%

Forecasting hourly emergency department arrival using time series analysis

Choudhury

Urena

2020

British Journal of Healthcare Management

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Background/aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.

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Section: Framework Explaining the Methodsmentioning

confidence: 99%

Forecasting hourly emergency department arrival using time series analysis

Choudhury

Urena

2020

British Journal of Healthcare Management

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“…During the process, the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test [29] was used to determine whether the time series was stationary around a mean or linear trend or is nonstationary owing to a unit root, and white noise was tested using the Box-Ljung test. [30] Moreover, we employed the Jarque-Bera test [31] and the Anderson-Darling test for normality, [32] and the augmented Dickey-Fuller test [29] to determine its stationarity. This was then followed by plotting of the autocorrelation function and partial autocorrelation function to determine ARIMA (p, d, q), where p is the order of autoregression (AR), d is the lagged difference between the current and previous values, and q denotes the order of the moving average (MA).…”

Section: Forecasting Emergency Department Arrivalsmentioning

confidence: 99%

Hourly Forecasting of Emergency Department Arrivals – Time Series Analysis

Choudhury

Urena

2019

SSRN Journal

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Background: The stochastic behavior of patient arrival at an emergency department (ED) complicates the management of an ED. More than 50% of a hospital's ED capacity tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address the concern of stochastics ED arrivals, many researches has been done using yearly, monthly and weekly timeseries forecasting. Aim: Our research team believes that hourly time-series forecasting of the load can improve ED management by predicting the arrivals of future patients, and thus, can support strategic decisions in terms of quality enhancement. Methods: Our research does not involve any human subject, only ED admission data from January 2014 to August 2017 retrieved from the UnityPoint Health database. Autoregressive integrated moving average (ARIMA), Holt-Winters, TBATS, and neural network methods were implemented to forecast hourly ED patient arrival. Findings: ARIMA (3,0,0) (2,1,0) was selected as the best fit model with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified the Box-Ljung correlation test and the Jarque-Bera test for normality. The mean error (ME) and root mean square error (RMSE) were selected as performance measures. An ME of 1.001 and an RMSE of 1.55 were obtained. Conclusions: ARIMA can be used to provide hourly forecasts for ED arrivals and can be utilized as a decision support system in the healthcare industry. Application:This technique can be implemented in hospitals worldwide to predict ED patient arrival. Forecasting Emergency Department Arrivals

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Model Building for Autocorrelated Process Control: An Industrial Experience

Cited by 2 publications

References 13 publications

Forecasting hourly emergency department arrival using time series analysis

Forecasting hourly emergency department arrival using time series analysis

Hourly Forecasting of Emergency Department Arrivals – Time Series Analysis

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