Selected article for: "cumulative case and mortality case"
Author: Pavan Kumar; Ram Kumar Singh; Chintan Nanda; Himangshu Kalita; Shashikanta Patairiya; Yagya Datt Sharma; Meenu Rani; Akshaya Srikanth Bhagavathula
Title: Forecasting COVID-19 impact in India using pandemic waves Nonlinear Growth Models
  • Document date: 2020_4_2
    • ID: b9p5tqhl_20
    Snippet: Where y′ t is the differenced series, the "predictors" on the right-hand side include both lagged for the forecast for the future dependent value. Even it is the function of white noise and past white noise error. Both the combination will make the ARMA model, which deals with stationary data values. We are dealing with time-series non-stationary values, the data observed value means, and variance is not constant, so third component (integratin.....
    Document: Where y′ t is the differenced series, the "predictors" on the right-hand side include both lagged for the forecast for the future dependent value. Even it is the function of white noise and past white noise error. Both the combination will make the ARMA model, which deals with stationary data values. We are dealing with time-series non-stationary values, the data observed value means, and variance is not constant, so third component (integrating(I)(d)) was used to convert the observations using differencing series [12, 13] . The differencing order two observation was used for the model forecast for COVID-19 case cumulative incidence, mortality, and recovery to avoid any misleading observed value functions.

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