About: ARIMA and NAR based Prediction Model for Time Series Analysis of COVID-19 cases in India

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	ARIMA and NAR based Prediction Model for Time Series Analysis of COVID-19 cases in India
Creator	Gupta, Khan, Gupta, Rajiv Farhan, Mohammad Khan, Rajiv Mohammad Khan, Farhan
Source	Elsevier; PMC
abstract	Abstract 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 is 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 R2 values of 0.95. The NAR model architecture constitutes ten neurons, which is optimized using the Levenberg-Marquardt optimization training algorithm (LM) with the overall highest R2 values of 0.97.
has issue date	2020-06-29(xsd:dateTime)
bibo:doi	10.1016/j.jnlssr.2020.06.007
has license	els-covid
sha1sum (hex)	de0fc4107379df8cebe6c6f5f29beb2c4c4f0f41
schema:url	https://doi.org/10.1016/j.jnlssr.2020.06.007
resource representing a document's title	ARIMA and NAR based Prediction Model for Time Series Analysis of COVID-19 cases in India
has PubMed Central identifier	PMC7321776
resource representing a document's body	covid:de0fc4107379df8cebe6c6f5f29beb2c4c4f0f41#body_text
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