I have 1000 data points and can see there is quite a high level of autocorrelation.
If I look at the lag 1 data pairs (1, 2), (3, 4), (5, 6), ..., then the correlation is r = .62.
I put the raw data here. I'd like to transform the data to remove or reduce the autocorrelation.
The context for all this is that the data points are guesses made by individuals about some quantity. I know the true value of the quantity and want to see whether the average guess is better if I just leave the data autocorrelated, or if I remove the autocorrelation.
One idea I had for an approach was to get rid of the lag 1 autocorrelation first, and then to see if average accuracy improves, and then try to get rid of the lag 2 autocorrelation.
I thought maybe I could just throw out every second data point, and then naturally autocorrelation would reduce. However, ideally I'd like to find a way to remove autocorrelation without throwing away data.