Time-varying auto-regressive models for count time-series

09/13/2020
by   Arkaprava Roy, et al.
0

Count-valued time series data are routinely collected in many application areas. We are particularly motivated to study the count time series of daily new cases, arising from COVID-19 spread. First, we propose a Bayesian framework to study time-varying semiparametric AR(p) model for count and then extend it to propose a time-varying INGARCH model considering the rapid changes in the spread. We calculate posterior contraction rates of the proposed Bayesian methods with respect to average Hellinger metric. Our proposed structures of the models are amenable to Hamiltonian Monte Carlo (HMC) sampling for efficient computation. We substantiate our methods by simulations that show superiority compared to some of the close existing methods. Finally we analyze the daily time series data of newly confirmed cases to study its spread through different government interventions.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/05/2020

Bayesian semiparametric time varying model for count data to study the spread of the COVID-19 cases

Recent outbreak of the novel corona virus COVID-19 has affected all of o...
research
09/13/2020

Bayesian modelling of time-varying conditional heteroscedasticity

Conditional heteroscedastic (CH) models are routinely used to analyze fi...
research
07/22/2022

Time-Varying Poisson Autoregression

In this paper we propose a new time-varying econometric model, called Ti...
research
12/18/2018

A new time-varying model for forecasting long-memory series

In this work we propose a new class of long-memory models with time-vary...
research
04/19/2019

An Alternative Data-Driven Prediction Approach Based on Real Option Theories

This paper presents a new prediction model for time series data by integ...
research
02/26/2021

Exploring the space-time pattern of log-transformed infectious count of COVID-19: a clustering-segmented autoregressive sigmoid model

At the end of April 20, 2020, there were only a few new COVID-19 cases r...
research
06/24/2022

Bayesian Circular Lattice Filters for Computationally Efficient Estimation of Multivariate Time-Varying Autoregressive Models

Nonstationary time series data exist in various scientific disciplines, ...

Please sign up or login with your details

Forgot password? Click here to reset