About: In this article, we analyze the growth pattern of Covid-19 pandemic in India from March 4th to May 15th using regression analysis (exponential and polynomial), auto-regressive integrated moving averages (ARIMA) model as well as Exponential Smoothing and Holt-Winters models. We found that the growth of Covid-19 cases follows a power regime of (t2,t..)after the exponential growth. We have found the optimal change points from where the covid-19 cases shifts their course of growth from exponential to quadratic and then quadratic to linear. We have also found the best fitted regression models using the various criteria like- significant p-values, coefficients of determination R2 values and ANOVA etc. Further, we have searched the best fitting ARIMA model for the data using the AIC (Akaike Information Criterion) and CAIC (Consistent Akaike Information Criterion) and forecasted the number of cases for future days. We have used the usual exponential smoothing and Holt-Winters models for the data. We further found that the ARIMA(2,2,0) model is the best-fitting model for Covid-19 cases in India.   Goto Sponge  NotDistinct  Permalink

An Entity of Type : fabio:Abstract, within Data Space : wasabi.inria.fr associated with source document(s)

AttributesValues
type
value
  • In this article, we analyze the growth pattern of Covid-19 pandemic in India from March 4th to May 15th using regression analysis (exponential and polynomial), auto-regressive integrated moving averages (ARIMA) model as well as Exponential Smoothing and Holt-Winters models. We found that the growth of Covid-19 cases follows a power regime of (t2,t..)after the exponential growth. We have found the optimal change points from where the covid-19 cases shifts their course of growth from exponential to quadratic and then quadratic to linear. We have also found the best fitted regression models using the various criteria like- significant p-values, coefficients of determination R2 values and ANOVA etc. Further, we have searched the best fitting ARIMA model for the data using the AIC (Akaike Information Criterion) and CAIC (Consistent Akaike Information Criterion) and forecasted the number of cases for future days. We have used the usual exponential smoothing and Holt-Winters models for the data. We further found that the ARIMA(2,2,0) model is the best-fitting model for Covid-19 cases in India.
Subject
  • Actuarial science
  • Chart overlays
  • Time series
  • 2019 disasters in China
part of
is abstract of
is hasSource of
Faceted Search & Find service v1.13.91 as of Mar 24 2020


Alternative Linked Data Documents: Sponger | ODE     Content Formats:       RDF       ODATA       Microdata      About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data]
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software