The first differences transformation might be tried if autocorrelation is found in a time-series data set.

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The first differences transformation is a commonly used method to remove autocorrelation in a time-series data set. It involves subtracting each data point from its predecessor to create a new series of data with no trend or pattern in the correlation between adjacent values. This method helps to eliminate the effects of seasonality and trend on the data, making it easier to analyze and model.

The First Differences Transformation Might Be Tried If Autocorrelation Is