EVDHM-ARIMA-Based Time Series Forecasting Model and Its Application for COVID-19 Cases

ABSTRACT

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. The Phillips-Perron test (PPT) is used to define the nonstationarity of time series. EVDHM is applied over a time series to decompose it into respective subcomponents and reduce the nonstationarity. ARIMA-based model is designed to forecast the future values for each subcomponent. The forecast values of each subcomponent are added to get the final output values. The optimized value of ARIMA parameters for each subcomponent is obtained using a genetic algorithm (GA) for minimum values of Akaike information criterion (AIC). Model performance is evaluated by estimating the future values of daily new cases of the recent pandemic disease COVID-19 for India, USA, and Brazil. The high efficacy of the proposed method is convinced with the results.