EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases.The time-series forecasting makes a substantial contribution in timely decision-making.  In this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) along with the autoregressive integrated moving average (ARIMA) is applied to develop a forecasting model for nonstationary time series.  EVDHM is applied over a time series to decompose it into respective subcomponents and reduce the nonstationarity. 
EVDHM
EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases.The time-series forecasting makes a substantial contribution in timely decision-making.  In this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) along with the autoregressive integrated moving average (ARIMA) is applied to develop a forecasting model for nonstationary time series.  ARIMA-based model is designed to forecast the future values for each subcomponent.  The optimized value of ARIMA parameters for each subcomponent is obtained using a genetic algorithm (GA) for minimum values of Akaike information criterion (AIC). 
ARIMA
The Phillips-Perron test (PPT) is used to define the nonstationarity of time series. 
Phillips-Perron test (PPT)
The optimized value of ARIMA parameters for each subcomponent is obtained using a genetic algorithm (GA) for minimum values of Akaike information criterion (AIC). 
Genetic Algorithm (GA)
The optimized value of ARIMA parameters for each subcomponent is obtained using a genetic algorithm (GA) for minimum values of Akaike information criterion (AIC). 
Akaike information criterion (AIC)