scholarly journals On the effect of COVID-19 pandemic in the excess of human mortality. The case of Brazil and Spain

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0255909
Author(s):  
Jorge A. Borrego–Morell ◽  
Edmundo J. Huertas ◽  
Nuria Torrado
Keyword(s):  
Time Series ◽  
Nonlinear Time Series ◽  
Time Series Forecasting ◽  
Unexpected Event ◽  
Human Mortality ◽  
The World ◽  
Linear And Nonlinear ◽  
First Time ◽  
Forecasting Techniques

Excess of deaths is a technique used in epidemiology to assess the deaths caused by an unexpected event. For the present COVID–19 pandemic, we discuss the performance of some linear and nonlinear time series forecasting techniques widely used for modeling the actual pandemic and provide estimates for this metric from January 2020 to April 2021. We apply the results obtained to evaluate the evolution of the present pandemic in Brazil and Spain, which allows in particular to compare how well (or bad) these countries have managed the pandemic. For Brazil, our calculations refute the claim made by some officials that the present pandemic is “a little flu”. Some studies suggest that the virus could be lying dormant across the world before been detected for the first time. In that regard, our results show that there is no evidence of deaths by the virus in 2019.

ESTIMATION OF CHANGE-POINTS IN LINEAR AND NONLINEAR TIME SERIES MODELS

2014 ◽  
Vol 32 (2) ◽  
pp. 402-430 ◽  
Author(s):  
Shiqing Ling
Keyword(s):  
Time Series ◽  
Asymptotic Theory ◽  
Nonlinear Time Series ◽  
Change Points ◽  
Time Series Models ◽  
Finite Sample ◽  
Asymptotically Normal ◽  
Estimated Parameters ◽  
Linear And Nonlinear ◽  
Economic Studies

This paper develops an asymptotic theory for estimated change-points in linear and nonlinear time series models. Based on a measurable objective function, it is shown that the estimated change-point converges weakly to the location of the maxima of a double-sided random walk and other estimated parameters are asymptotically normal. When the magnitude d of changed parameters is small, it is shown that the limiting distribution can be approximated by the known distribution as in Yao (1987, Annals of Statistics 15, 1321–1328). This provides a channel to connect our results with those in Picard (1985, Advances in Applied Probability 17, 841–867) and Bai, Lumsdaine, and Stock (1998, Review of Economic Studies 65, 395–432), where the magnitude of changed parameters depends on the sample size n and tends to zero as n → ∞. The theory is applied for the self-weighted QMLE and the local QMLE of change-points in ARMA-GARCH/IGARCH models. A simulation study is carried out to evaluate the performance of these estimators in the finite sample.

scholarly journals Volatility of linear and nonlinear time series

2005 ◽  
Vol 72 (1) ◽  
Author(s):  
Tomer Kalisky ◽  
Yosef Ashkenazy ◽  
Shlomo Havlin
Keyword(s):  
Time Series ◽  
Nonlinear Time Series ◽  
Linear And Nonlinear

scholarly journals Stochastic nonlinear time series forecasting using time-delay reservoir computers: Performance and universality

2014 ◽  
Vol 55 ◽  
pp. 59-71 ◽  
Author(s):  
Lyudmila Grigoryeva ◽  
Julie Henriques ◽  
Laurent Larger ◽  
Juan-Pablo Ortega
Keyword(s):  
Time Series ◽  
Time Delay ◽  
Nonlinear Time Series ◽  
Time Series Forecasting

scholarly journals An Effective Prediction on COVID-19 Prevalence for India and Japan using Fbprophet Model

2021 ◽  
pp. 16-28
Author(s):  
Md. Mehedi Rahman Rana ◽  
Farjana Rahman ◽  
Jabed Al Faysal ◽  
Md. Anisur Rahman
Keyword(s):  
Time Series ◽  
Infection Rate ◽  
Satisfactory Result ◽  
Death Rate ◽  
Large Scale ◽  
Time Series Forecasting ◽  
Economic Life ◽  
Substantial Influence ◽  
The World

Coronavirus has become a significant concern for the whole world. It has had a substantial influence on our social and economic life. The infection rate is rapidly increasing at every moment throughout the world. At present, predicting coronavirus has become one of the challenging issues for us. As the pace of COVID-19 detection increases, so does the death rate. This research predicts the number of coronavirus detection and deaths using Fbprophet, a tool designed to assist in performing time series forecasting at a large scale. Two major affected countries, India and Japan, have been taken into consideration in our approach.  Using the prophet model, a prediction is performed on the number of total cases, new cases, total deaths and new deaths. This model works considerably well, and it has given a satisfactory result that may help the authority in taking early and appropriate decisions depending on the predicted COVID situation.

scholarly journals Time-Series Forecasting and Analysis of COVID-19 Outbreak in Highly Populated Countries

2022 ◽  
Vol 13 (2) ◽  
pp. 1-17
Author(s):  
Arunkumar P. M. ◽  
Lakshmana Kumar Ramasamy ◽  
Amala Jayanthi M.
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Research Area ◽  
Time Series Forecasting ◽  
Disease Spread ◽  
Time Period ◽  
The World ◽  
Auto Regressive ◽  
Important Research Area

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.

Sign in / Sign up

Export Citation Format

Share Document