Author: Taranjot Kaur; Sukanta Sarkar; Sourangsu Chowdhury; Sudipta Kumar Sinha; Mohit Kumar Jolly; Partha Sharathi Dutta
Title: Anticipating the novel coronavirus disease (COVID-19) pandemic Document date: 2020_4_10
ID: 1xenvfcd_50
Snippet: Detrending. Often, non-stationarities in the data lead to false indications of impending transitions. To overcome this, we obtain the residual time-series by subtracting a Gaussian kernel smoothing function from the empirical time-series [14] . Further, we estimate the return rate and autocorrelation at first lag for the residual time-series choosing a rolling window of 3 4 the size of the time-series data (i.e., 75 %) for Italy and 50% for the o.....
Document: Detrending. Often, non-stationarities in the data lead to false indications of impending transitions. To overcome this, we obtain the residual time-series by subtracting a Gaussian kernel smoothing function from the empirical time-series [14] . Further, we estimate the return rate and autocorrelation at first lag for the residual time-series choosing a rolling window of 3 4 the size of the time-series data (i.e., 75 %) for Italy and 50% for the other countries. We choose the filtering bandwidth using Silverman's thumb rule to avoid any overfit.
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