I am using ML regression in Sklearn to predict a final cost (in a separate df). Before fitting a linear regression I went to test the assumptions of linear regression and have a problem with autocorrelation. Running an autocorrelation test gave me this
As you can see the Durbin Watson is too low. Someone suggested to me since some of my variables may be affected by time, to use ARIMA models to fix it. So to test if any of my variables were non-stationary I used an ADFULLER test and got these results.
The P-value of 0 for all of the variables indicates that the variables are already stationary meaning time shouldn't be the issue. Does anyone have a suggestion to get rid of this autocorrelation in my data?