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With this in thoughts, we carried out a large simulation study to analyze the impression of chronological uncertainty on a probably useful time-series technique. The methodology is a sort of regression involving a prediction algorithm called the Poisson Exponentially Weighted Moving Average (PEMWA). It is designed to be used with count time-series information, which makes it relevant to a variety of questions about human-environment interaction in deep time. Our simulations suggest that the PEWMA technique can usually appropriately establish relationships between time-series despite chronological uncertainty. When two time-series are correlated with a coefficient of 0.25, the tactic is prepared to identify that relationship appropriately 20–30% of the time, offering the time-series comprise low noise levels. With correlations of round zero.5, it’s capable of accurately identifying correlations despite chronological uncertainty greater than 90% of the time.