Abstract
A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.
Highlights
A novel corona virus, COVID-19, is spreading across different countries in an alarming proportion, and it has become a major threat to the existence of human community
The emergence of novel corona virus is identified from the Wuhan City, Hubei province in China during December 2019 and subsequently renamed as COVID-19 by World health organization
Holt–Winters Additive Model (HWAAS), Auto-regressive integrated moving average (ARIMA), TBAT, Prophet, DeepAR and N-Beats and Vector Auto regression (VAR) are few models used by researchers around the world for time-series forecasting(Papastefanopoulos,2020)
Summary
The emergence of novel corona virus is identified from the Wuhan City, Hubei province in China during December 2019 and subsequently renamed as COVID-19 by World health organization. The time-series analysis and forecasting for COVID-19 disease outbreak is an emerging research paradigm that requires deep knowledge and better experimentations for interpreting the trend and evaluating the predictions. Holt–Winters Additive Model (HWAAS), Auto-regressive integrated moving average (ARIMA), TBAT, Prophet, DeepAR and N-Beats and Vector Auto regression (VAR) are few models used by researchers around the world for time-series forecasting(Papastefanopoulos,2020). In HWAAS model, trend and seasonal variation of the data are taken in to account This method is an advanced model proposed by adopting added features to Holt’s exponential smoothing. A deep neural architecture consisting of forward and backward residual links is used by N-Beats model (Oreshkin et al.,2019) In this method, generic architecture and an interpretable architecture are used in tandem and dual residual stacking results are observed. In ARIMA model, information in the past values of the time series can alone be used to predict the future values
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