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ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India
In this paper, we have applied the univariate time series model to predict the number of COVID-19 infected cases that can be expected in upcoming days in India. We adopted an Auto-Regressive Integrated Moving Average (ARIMA) model on the data collected from 31st January 2020 to 25th March 2020 and v...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
China Science Publishing & Media Ltd. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7321776/ http://dx.doi.org/10.1016/j.jnlssr.2020.06.007 |
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author | Khan, Farhan Mohammad Gupta, Rajiv |
author_facet | Khan, Farhan Mohammad Gupta, Rajiv |
author_sort | Khan, Farhan Mohammad |
collection | PubMed |
description | In this paper, we have applied the univariate time series model to predict the number of COVID-19 infected cases that can be expected in upcoming days in India. We adopted an Auto-Regressive Integrated Moving Average (ARIMA) model on the data collected from 31st January 2020 to 25th March 2020 and verified it using the data collected from 26th March 2020 to 04th April 2020. A nonlinear autoregressive (NAR) neural network was developed to compare the accuracy of predicted models. The model has been used for daily prediction of COVID-19 cases for next 50 days without any additional intervention. Statistics from various sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/ are used for the study. The results showed an increasing trend in the actual and forecasted numbers of COVID-19 cases with approximately 1500 cases per day, based on available data as on 04th April 2020. The appropriate ARIMA (1,1,0) model was selected based on the Bayesian Information Criteria (BIC) values and the overall highest R(2) values of 0.95. The NAR model architecture constitutes ten neurons, which was optimized using the Levenberg-Marquardt optimization training algorithm (LM) with the overall highest R(2) values of 0.97. |
format | Online Article Text |
id | pubmed-7321776 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | China Science Publishing & Media Ltd. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-73217762020-06-29 ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India Khan, Farhan Mohammad Gupta, Rajiv Journal of Safety Science and Resilience Article In this paper, we have applied the univariate time series model to predict the number of COVID-19 infected cases that can be expected in upcoming days in India. We adopted an Auto-Regressive Integrated Moving Average (ARIMA) model on the data collected from 31st January 2020 to 25th March 2020 and verified it using the data collected from 26th March 2020 to 04th April 2020. A nonlinear autoregressive (NAR) neural network was developed to compare the accuracy of predicted models. The model has been used for daily prediction of COVID-19 cases for next 50 days without any additional intervention. Statistics from various sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/ are used for the study. The results showed an increasing trend in the actual and forecasted numbers of COVID-19 cases with approximately 1500 cases per day, based on available data as on 04th April 2020. The appropriate ARIMA (1,1,0) model was selected based on the Bayesian Information Criteria (BIC) values and the overall highest R(2) values of 0.95. The NAR model architecture constitutes ten neurons, which was optimized using the Levenberg-Marquardt optimization training algorithm (LM) with the overall highest R(2) values of 0.97. China Science Publishing & Media Ltd. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. 2020-09 2020-06-29 /pmc/articles/PMC7321776/ http://dx.doi.org/10.1016/j.jnlssr.2020.06.007 Text en © 2020 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Khan, Farhan Mohammad Gupta, Rajiv ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India |
title | ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India |
title_full | ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India |
title_fullStr | ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India |
title_full_unstemmed | ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India |
title_short | ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India |
title_sort | arima and nar based prediction model for time series analysis of covid-19 cases in india |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7321776/ http://dx.doi.org/10.1016/j.jnlssr.2020.06.007 |
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