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It takes a very long time to get to the underside of

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With this in thoughts, we performed a large simulation examine to investigate the influence of chronological uncertainty on a doubtlessly useful time-series technique. The methodology is a sort of regression involving a prediction algorithm known as the Poisson Exponentially Weighted Moving Average (PEMWA). It is designed to be used with rely time-series data, which makes it applicable to a variety of questions about human-environment interaction in deep time. Our simulations suggest that the PEWMA technique can usually correctly determine relationships between time-series despite chronological uncertainty. When two time-series are correlated with a coefficient of 0.25, the method is able to identify that relationship accurately 20–30% of the time, providing the time-series comprise low noise levels. With correlations of around 0.5, it is able to accurately figuring out correlations despite chronological uncertainty greater than 90% of the time.