Nonstationary time series forecasting using optimized-EVDHM-ARIMA for COVID-19 - Nagvanshi, Suraj Singh; Kaur, Inderjeet; Agarwal, Charu; Sharma, Ashish (Frontiers in Big Data, 2023)
The Coronavirus (COVID-19) outbreak swept the world, infected millions of people, and caused many deaths. Multiple COVID-19 variations have been discovered since the initial case in December 2019, indicating that COVID-19 is highly mutable. COVID-19 variation 'XE' is the most current of all COVID-19 variants found in January 2022. It is vital to detect the virus transmission rate and forecast instances of infection to be prepared for all scenarios, prepare healthcare services, and avoid deaths. Time-series forecasting helps predict future infected cases and determine the virus transmission rate to make timely decisions. A forecasting model for nonstationary time series has been created in this paper. The model comprises an optimized EigenValue Decomposition of Hankel Matrix (EVDHM) and an optimized AutoRegressive Integrated Moving Average (ARIMA). The Phillips Perron Test (PPT) has been used to determine whether a time series is nonstationary. A time series has been decomposed into components using EVDHM, and each component has been forecasted using ARIMA. The final forecasts have been formed by combining the predicted values of each component. A Genetic Algorithm (GA) to select ARIMA parameters resulting in the lowest Akaike Information Criterion (AIC) values has been used to discover the best ARIMA parameters. Another genetic algorithm has been used to optimize the decomposition results of EVDHM that ensures the minimum nonstationarity and maximal utilization of eigenvalues for each decomposed component.