One of the ways to overcome the autocorrelation problem in a regression forecasting model is to transform the variables by taking the first-differences.

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Taking the first differences of the variables in a regression model is a common method to reduce or eliminate autocorrelation, as it focuses on the changes between consecutive observations rather than the levels themselves, thus potentially removing the autocorrelation.

One of the Ways to Overcome the Autocorrelation Problem in a Regression