صورة الغلاف

Forecasting patient admission in orthopedic clinic at a hospital in Kuantan using autoregressive integrated moving average (ARIMA) models

This study is an attempt to examine empirically the best ARIMA model for forecasting. The monthly time series data routinely-collected at Orthopedic clinic from January 2013 until June 2018 have been used for this purpose. At first the stationarity condition of the data series is observed by ACF and...

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محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Mohamed, Bahari, Mohamad, Meriati
التنسيق: Conference or Workshop Item
اللغة:English
English
منشور في: IOP Publishing Ltd 2020
الموضوعات:
QC Physics
الوصول للمادة أونلاين:http://irep.iium.edu.my/81910/13/81910_Forecasting%20patient%20admission%20in%20orthopedic%20clinic%20at%20a%20hospital.pdf
http://irep.iium.edu.my/81910/14/81910_Forecasting%20patient%20admission%20in%20orthopedic%20clinic%20at%20a%20hospital%20SCOPUS.pdf
http://irep.iium.edu.my/81910/
https://iopscience.iop.org/article/10.1088/1742-6596/1529/5/052090/pdf
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spelling my.iium.irep.819102021-01-19T21:05:25Z http://irep.iium.edu.my/81910/ Forecasting patient admission in orthopedic clinic at a hospital in Kuantan using autoregressive integrated moving average (ARIMA) models Mohamed, Bahari Mohamad, Meriati QC Physics This study is an attempt to examine empirically the best ARIMA model for forecasting. The monthly time series data routinely-collected at Orthopedic clinic from January 2013 until June 2018 have been used for this purpose. At first the stationarity condition of the data series is observed by ACF and PACF plots, then checked using the Ljung-Box-Pierce Qstatistic. It has been found that the monthly time series data of the Orthopedic clinic are stationary. The best ARIMA model has been selected by using the MAPE. To select the best ARIMA model the data split into two periods, viz. estimation period and validation period. The model for which the values of MAPE are smallest is considered as the best model. Hence, ARIMA (1, 0, 0) is found as the best model for forecasting the Orthopedic clinic data series. The out of sample forecast by using ARIMA (1, 0, 0) model indicated a fluctuation of monthly orthopedic patients demand, from lowest was 294 and the highest was 299 patients that could receive treatment from the clinic in a month. IOP Publishing Ltd 2020-06-17 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/81910/13/81910_Forecasting%20patient%20admission%20in%20orthopedic%20clinic%20at%20a%20hospital.pdf application/pdf en http://irep.iium.edu.my/81910/14/81910_Forecasting%20patient%20admission%20in%20orthopedic%20clinic%20at%20a%20hospital%20SCOPUS.pdf Mohamed, Bahari and Mohamad, Meriati (2020) Forecasting patient admission in orthopedic clinic at a hospital in Kuantan using autoregressive integrated moving average (ARIMA) models. In: 2nd Joint International Conference on Emerging Computing Technology and Sports (JICETS 2019), 25th-27th November 2019, Bandung, Indonesia. https://iopscience.iop.org/article/10.1088/1742-6596/1529/5/052090/pdf doi:10.1088/1742-6596/1529/5/052090
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic QC Physics
spellingShingle QC Physics
Mohamed, Bahari
Mohamad, Meriati
Forecasting patient admission in orthopedic clinic at a hospital in Kuantan using autoregressive integrated moving average (ARIMA) models
description This study is an attempt to examine empirically the best ARIMA model for forecasting. The monthly time series data routinely-collected at Orthopedic clinic from January 2013 until June 2018 have been used for this purpose. At first the stationarity condition of the data series is observed by ACF and PACF plots, then checked using the Ljung-Box-Pierce Qstatistic. It has been found that the monthly time series data of the Orthopedic clinic are stationary. The best ARIMA model has been selected by using the MAPE. To select the best ARIMA model the data split into two periods, viz. estimation period and validation period. The model for which the values of MAPE are smallest is considered as the best model. Hence, ARIMA (1, 0, 0) is found as the best model for forecasting the Orthopedic clinic data series. The out of sample forecast by using ARIMA (1, 0, 0) model indicated a fluctuation of monthly orthopedic patients demand, from lowest was 294 and the highest was 299 patients that could receive treatment from the clinic in a month.
format Conference or Workshop Item
author Mohamed, Bahari
Mohamad, Meriati
author_facet Mohamed, Bahari
Mohamad, Meriati
author_sort Mohamed, Bahari
title Forecasting patient admission in orthopedic clinic at a hospital in Kuantan using autoregressive integrated moving average (ARIMA) models
title_short Forecasting patient admission in orthopedic clinic at a hospital in Kuantan using autoregressive integrated moving average (ARIMA) models
title_full Forecasting patient admission in orthopedic clinic at a hospital in Kuantan using autoregressive integrated moving average (ARIMA) models
title_fullStr Forecasting patient admission in orthopedic clinic at a hospital in Kuantan using autoregressive integrated moving average (ARIMA) models
title_full_unstemmed Forecasting patient admission in orthopedic clinic at a hospital in Kuantan using autoregressive integrated moving average (ARIMA) models
title_sort forecasting patient admission in orthopedic clinic at a hospital in kuantan using autoregressive integrated moving average (arima) models
publisher IOP Publishing Ltd
publishDate 2020
url http://irep.iium.edu.my/81910/13/81910_Forecasting%20patient%20admission%20in%20orthopedic%20clinic%20at%20a%20hospital.pdf
http://irep.iium.edu.my/81910/14/81910_Forecasting%20patient%20admission%20in%20orthopedic%20clinic%20at%20a%20hospital%20SCOPUS.pdf
http://irep.iium.edu.my/81910/
https://iopscience.iop.org/article/10.1088/1742-6596/1529/5/052090/pdf
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