Selected article for: "arima model and incidence traffic"
Author: Zaixing Shi; Ya Fang
Title: Temporal relationship between outbound traffic from Wuhan and the 2019 coronavirus disease (COVID-19) incidence in China
  • Document date: 2020_3_17
    • ID: hrrzztt5_16
    Snippet: We used time series analyses, including cross-correlation function (CCF) and autoregressive integrated moving average (ARIMA) model [8] , to examine time-lagged association between traffic volume and COVID-19 incidence. Time series analysis is appropriate because it examines the correlation between two time series data and makes predictions while accounting for any autocorrelation structure within each time series and any shared trends. We used t.....
    Document: We used time series analyses, including cross-correlation function (CCF) and autoregressive integrated moving average (ARIMA) model [8] , to examine time-lagged association between traffic volume and COVID-19 incidence. Time series analysis is appropriate because it examines the correlation between two time series data and makes predictions while accounting for any autocorrelation structure within each time series and any shared trends. We used the CCF to understand the time-lagged correlation between traffic volume and COVID-19 incidence.

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