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With this in mind, we conducted a large simulation research to investigate the impression of chronological uncertainty on a probably useful time-series technique. The technique is a sort of regression involving a prediction algorithm called the Poisson Exponentially Weighted Moving Average (PEMWA). It is designed to be used with depend time-series information, which makes it applicable to a broad range of questions about human-environment interplay in deep time. Our simulations counsel that the PEWMA method can typically accurately determine relationships between time-series regardless of chronological uncertainty. When two time-series are correlated with a coefficient of zero.25, the strategy is ready to identify that relationship correctly 20–30% of the time, providing the time-series comprise low noise levels. With correlations of around zero.5, it’s able to accurately identifying correlations regardless of chronological uncertainty greater than 90% of the time.