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_17
Snippet: A prewhitening process was applied to the time-series data to avoid common trends between traffic and CVID-19 incidence [9] . The prewhitening was conducted by first fitting an ARIMA model on the x-variable and calculating the residuals, and then filtering the y variable using the x-variable ARIMA model. The CCF assesses the correlation between the residuals from the xvariable ARIMA model and the filtered y values. In our case, the x variable is .....
Document: A prewhitening process was applied to the time-series data to avoid common trends between traffic and CVID-19 incidence [9] . The prewhitening was conducted by first fitting an ARIMA model on the x-variable and calculating the residuals, and then filtering the y variable using the x-variable ARIMA model. The CCF assesses the correlation between the residuals from the xvariable ARIMA model and the filtered y values. In our case, the x variable is the daily traffic volume time series and the y variable is the COVID-19 case time series. The non-seasonal ARIMA model is generally denoted as ARIMA ( , , ), in which is the order of the autoregression (AR) component, is the order of the differencing process to form a stationary times series, and is the order of the moving average (MA) process. In an ARIMA model, the value of at time is estimated as:
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