Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19
Autor
Singh, Sarbjit
Singh Parmar, Kulwinder
Kumar, Jatinder
Singh Makkhan, Sidhu Jitendra
Institución
Resumen
Everywhere around the globe, the hot topic of discussion today is the ongoing and fast-spreading coronavirus disease (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARSCOV-2). Earlier detected in Wuhan, Hubei province, in China in December 2019, the deadly virus engulfed
China and some neighboring countries, which claimed thousands of lives in February 2020. The proposed
hybrid methodology involves the application of discreet wavelet decomposition to the dataset of deaths
due to COVID-19, which splits the input data into component series and then applying an appropriate
econometric model to each of the component series for making predictions of death cases in future.
ARIMA models are well known econometric forecasting models capable of generating accurate forecasts
when applied on wavelet decomposed time series. The input dataset consists of daily death cases from
most affected five countries by COVID-19, which is given to the hybrid model for validation and to make
one month ahead prediction of death cases. These predictions are compared with that obtained from an
ARIMA model to estimate the performance of prediction. The predictions indicate a sharp rise in death
cases despite various precautionary measures taken by governments of these countries