Abstract
AbstractCOVID-19 is an infectious disease that has spread over the world since the first case was discovered in China in December 2019. Multiple variants of COVID-19 have been discovered in the last two years, indicating that it is highly mutable. The most recent variant is omicron, which has similar transmissibility to the delta variant and so has a significant risk of producing a third wave in India. This study analyzes five distinct time series forecasting models: autoregression (AR), exponential smoothing (ES), multilayer perceptron (MLP), long-short term memory (LSTM), autoregressive integrated moving average (ARIMA), and their hybrid models. The purpose of this research is to find the best machine learning model for forecasting COVID-19 cases, as the number of novel variant omicron cases in India is on the rise. As a result, it is necessary to forecast COVID-19 cases to make appropriate precautionary actions in order to avert the third wave of COVID-19 in India.KeywordsCOVID-19AutoregressionExponential smoothingMultilayer perceptronLong-short term memoryAutoregressive integrated moving average
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