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The Perils of Ignoring Autocorrelation: Distorting True Cause & Effect Conclusions
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In research fields ranging from climate to seismology, analysts routinely examine time series data—records of how variables like temperatures change or micro-seismic signals change over time. Identifying meaningful patterns in these series provides insight for everything from stock predictions to climate models.
However, a common pitfall arises in time series analysis: the problem of autocorrelation.
More specifically, in a time series that exhibits autocorrelation, the data values at any given time are statistically dependent on prior values in the same series. This violates the assumption of independence that many statistical techniques rely on.
Positive autocorrelation implies that a high value in the series will likely be followed by another high value, and vice versa for low values. This may reflect an underlying cyclical pattern or inertia in the system generating the time series.
While sometimes subtle, ignoring autocorrelation can severely undermine the validity of analysis results for just about every area of research that deals with time series data.
The climate research field, for example, is rife with datasets that exhibit excessive autocorrelation due to non-stationarity data.
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