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The problem that you have "I am unable to use classic linear regression due to strong temporal (serial) auto-correlation among values." is in reality an opportunity. I took your 27 values and used AUTOBOX a piece of software (which I have helped develop) that can (optionally) automatically determine a possible model. Here is the actual/fit and forecast graph . The ACF of the residuals is here with residual plot here . The model is here and here and here . Two coefficients aptly describe the data with estimated "trend" aka "drift" i.e. period to period differential of -.596. Note that this is one kind of trend where your model used the counting numbers 1,2,...27 as a predictor variable. If your data suggested that kind of trend then the software would have found it to be more applicable. I will try and find an earlier post of mine which fully detailed/contrasted these two kinds of trends. Here Identifying a stochastic trend model and Detecting initial trend or outliers

The problem that you have "I am unable to use classic linear regression due to strong temporal (serial) auto-correlation among values." is in reality an opportunity. I took your 27 values and used AUTOBOX a piece of software (which I have helped develop) that can (optionally) automatically determine a possible model. Here is the actual/fit and forecast graph . The ACF of the residuals is here with residual plot here . The model is here and here and here . Two coefficients aptly describe the data with estimated "trend" aka "drift" i.e. period to period differential of -.596. Note that this is one kind of trend where your model used the counting numbers 1,2,...27 as a predictor variable. If your data suggested that kind of trend then the software would have found it to be more applicable. I will try and find an earlier post of mine which fully detailed/contrasted these two kinds of trends. Here Identifying a stochastic trend model and Detecting initial trend or outliers

The problem that you have "I am unable to use classic linear regression due to strong temporal (serial) auto-correlation among values." is in reality an opportunity. I took your 27 values and used AUTOBOX a piece of software (which I have helped develop) that can (optionally) automatically determine a possible model. Here is the actual/fit and forecast graph . The ACF of the residuals is here with residual plot here . The model is here and here and here . Two coefficients aptly describe the data with estimated "trend" i.e. period to period differential of -.596. Note that this is one kind of trend where your model used the counting numbers 1,2,...27 as a predictor variable. If your data suggested that kind of trend then the software would have found it to be more applicable. I will try and find an earlier post of mine which fully detailed/contrasted these two kinds of trends. Here Identifying a stochastic trend model and Detecting initial trend or outliers

The problem that you have "I am unable to use classic linear regression due to strong temporal (serial) auto-correlation among values." is in reality an opportunity. I took your 27 values and used AUTOBOX a piece of software (which I have helped develop) that can (optionally) automatically determine a possible model. Here is the actual/fit and forecast graph . The ACF of the residuals is here with residual plot here . The model is here and here and here . Two coefficients aptly describe the data with estimated "trend" aka "drift" i.e. period to period differential of -.596. Note that this is one kind of trend where your model used the counting numbers 1,2,...27 as a predictor variable. If your data suggested that kind of trend then the software would have found it to be more applicable. I will try and find an earlier post of mine which fully detailed/contrasted these two kinds of trends. Here Identifying a stochastic trend model and Detecting initial trend or outliers

The problem that you have "I am unable to use classic linear regression due to strong temporal (serial) auto-correlation among values." is in reality an opportunity. I took your 27 values and used AUTOBOX a piece of software (which I have helped develop) that can (optionally) automatically determine a possible model. Here is the actual/fit and forecast graph . The ACF of the residuals is here with residual plot here . The model is here and here and here . Two coefficients aptly describe the data with estimated "trend" i.e. period to period differential of -.596. Note that this is one kind of trend where your model used the counting numbers 1,2,...27 as a predictor variable. If your data suggested that kind of trend then the software would have found it to be more applicable. I will try and find an earlier post of mine which fully detailed/contrasted these two kinds of trends. Here Identifying a stochastic trend model and

The problem that you have "I am unable to use classic linear regression due to strong temporal (serial) auto-correlation among values." is in reality an opportunity. I took your 27 values and used AUTOBOX a piece of software (which I have helped develop) that can (optionally) automatically determine a possible model. Here is the actual/fit and forecast graph . The ACF of the residuals is here with residual plot here . The model is here and here and here . Two coefficients aptly describe the data with estimated "trend" i.e. period to period differential of -.596. Note that this is one kind of trend where your model used the counting numbers 1,2,...27 as a predictor variable. If your data suggested that kind of trend then the software would have found it to be more applicable. I will try and find an earlier post of mine which fully detailed/contrasted these two kinds of trends. Here Identifying a stochastic trend model and Detecting initial trend or outliers