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  1. Article ; Online: 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

    2020  Volume 1, Issue 1, Page(s) 12–18

    Keywords covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ISSN 2666-4496
    DOI 10.1016/j.jnlssr.2020.06.007
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India

    Farhan Mohammad Khan / Rajiv Gupta

    Journal of Safety Science and Resilience, Vol 1, Iss 1, Pp 12-

    2020  Volume 18

    Abstract: In this paper, we have applied the univariate time series model to predict the number of COVID-19 ... prediction of COVID-19 cases for next 50 days without any additional intervention. Statistics from various ... for the study. The results showed an increasing trend in the actual and forecasted numbers of COVID-19 cases ...

    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 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 R2 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 R2 values of 0.97.
    Keywords Time series ; Novel coronavirus ; SARS-CoV-2 ; Forecasting ; ARIMA ; NAR ; Risk in industry. Risk management ; HD61 ; covid19
    Subject code 310
    Language English
    Publishing date 2020-09-01T00:00:00Z
    Publisher KeAi Communications Co. Ltd.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Article: 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

    Abstract: In this paper, we have applied the univariate time series model to predict the number of COVID-19 ... prediction of COVID-19 cases for next 50 days without any additional intervention Statistics from various ... for the study The results showed an increasing trend in the actual and forecasted numbers of COVID-19 cases ...

    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 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 R2 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 R2 values of 0 97
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #621895
    Database COVID19

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