Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models

Background and objectives: Auto regressive integrated moving average (ARIMA) model is a popular model to forecast future values of a time series using the past values of the same series. However, if the variance of the time series varies with time, the 95% confidence interval estimated by the ARIMA...

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Autor principal:	Jian Sun, PhD
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	ARIMA COVID-19 Infectious disease SAS Time series analysis Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/9ccab298bf194360913eaff29a8bdb18
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spelling	oai:doaj.org-article:9ccab298bf194360913eaff29a8bdb182021-11-14T04:36:06ZForecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models2666-990010.1016/j.cmpbup.2021.100029https://doaj.org/article/9ccab298bf194360913eaff29a8bdb182021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666990021000288https://doaj.org/toc/2666-9900Background and objectives: Auto regressive integrated moving average (ARIMA) model is a popular model to forecast future values of a time series using the past values of the same series. However, if the variance of the time series varies with time, the 95% confidence interval estimated by the ARIMA will not be accurate. This study proposes a method to revise the ARIMA model to suit time series with heteroscedasticity. Methods: Multiple historical ARIMA models were constructed with publicly available COVID-19 data in Alberta, Canada. The time series between different time periods were applied for these models. The means and their 95% confidence intervals of the differences between the forecasted values and the corresponding actual values were computed. The forecasted values of the general ARIMA models were modified by adding these differences. Results: The average incident cases forecasted with the proposed method are lower than those with a general ARIMA model during the forecasted period. The 95% confidence intervals of the forecasted incidence with the proposed method are narrower. During the forecasted period (13 weeks) the average incidence was predicted to increase first and then decrease exponentially. Conclusion: The proposed method can be used to automatically specify the best ARIMA model, to fit time series with heteroscedasticity and to forecast longer period of the trends in the future. In the next 13 weeks, the Covid-19 incidence may decrease but not eliminate. To stop the transmission of infections eventually, persistent effects complying with accurate forecasts are necessary.Jian Sun, PhDElsevierarticleARIMACOVID-19Infectious diseaseSASTime series analysisComputer applications to medicine. Medical informaticsR858-859.7ENComputer Methods and Programs in Biomedicine Update, Vol 1, Iss , Pp 100029- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ARIMA COVID-19 Infectious disease SAS Time series analysis Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	ARIMA COVID-19 Infectious disease SAS Time series analysis Computer applications to medicine. Medical informatics R858-859.7 Jian Sun, PhD Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models
description	Background and objectives: Auto regressive integrated moving average (ARIMA) model is a popular model to forecast future values of a time series using the past values of the same series. However, if the variance of the time series varies with time, the 95% confidence interval estimated by the ARIMA will not be accurate. This study proposes a method to revise the ARIMA model to suit time series with heteroscedasticity. Methods: Multiple historical ARIMA models were constructed with publicly available COVID-19 data in Alberta, Canada. The time series between different time periods were applied for these models. The means and their 95% confidence intervals of the differences between the forecasted values and the corresponding actual values were computed. The forecasted values of the general ARIMA models were modified by adding these differences. Results: The average incident cases forecasted with the proposed method are lower than those with a general ARIMA model during the forecasted period. The 95% confidence intervals of the forecasted incidence with the proposed method are narrower. During the forecasted period (13 weeks) the average incidence was predicted to increase first and then decrease exponentially. Conclusion: The proposed method can be used to automatically specify the best ARIMA model, to fit time series with heteroscedasticity and to forecast longer period of the trends in the future. In the next 13 weeks, the Covid-19 incidence may decrease but not eliminate. To stop the transmission of infections eventually, persistent effects complying with accurate forecasts are necessary.
format	article
author	Jian Sun, PhD
author_facet	Jian Sun, PhD
author_sort	Jian Sun, PhD
title	Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models
title_short	Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models
title_full	Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models
title_fullStr	Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models
title_full_unstemmed	Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models
title_sort	forecasting covid-19 pandemic in alberta, canada using modified arima models
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/9ccab298bf194360913eaff29a8bdb18
work_keys_str_mv	AT jiansunphd forecastingcovid19pandemicinalbertacanadausingmodifiedarimamodels
_version_	1718429912941985792

Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models

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