Rolling windows
The rolling window approach partitions the univariate time series of interest into a window of data points within which each indicator is estimated. The window then ‘rolls’ along the time series one data point at a time to update the indicator estimate and generate a new time series of EWSs (Figure 2a). From this EWS time series, the Kendall’s Tau correlation of the EWS against time is used to generate ‘warnings’ (Figure 2b). Specifically, if a strong Tau correlation is found, this indicates an oncoming transition. The uniEWS function allows the user to specify the window size as a percentage of the time series’ length and returns both the time series of EWSs and the estimated Kendall’s Tau to be interpreted.