Trajectory of COVID-19 Data in India: Investigation and Project Using Artificial Neural Network, Fuzzy Time Series and ARIMA Models

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Pradeep Mishra
Chellai Fatih
Deepa Rawat
Saswati Sahu
Sagar Anand Pandey
M. Ray
Anurag Dubey
Olawale Monsur Sanusi


Due to the impact of Corona virus (COVID-19) pandemic that exists today, all countries, national and international organizations are in a continuous effort to find efficient and accurate statistical models for forecasting the future pattern of COVID infection. Accurate forecasting should help governments to take decisive decisions to master the pandemic spread.  In this article, we explored the COVID-19 database of India between 17th March to 1st July 2020, then we estimated two nonlinear time series models: Artificial Neural Network (ANN) and Fuzzy Time Series (FTS) by comparing them with ARIMA model. In terms of model adequacy, the FTS model out performs the ANN for the new cases and new deaths time series in India. We observed a short-term virus spread trend according to three forecasting models.Such findings help in more efficient preparation for the Indian health system.

Artificial neural network, ARIMA, COVID-19 forecasting, fuzzy time series, India

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How to Cite
Mishra, P., Fatih, C., Rawat, D., Sahu, S., Pandey, S. A., Ray, M., Dubey, A., & Sanusi, O. M. (2020). Trajectory of COVID-19 Data in India: Investigation and Project Using Artificial Neural Network, Fuzzy Time Series and ARIMA Models. Annual Research & Review in Biology, 35(9), 46-54.
Original Research Article


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