1.
Exploratory Data Analysis
1.3. EDA Techniques 1.3.3. Graphical Techniques: Alphabetic 1.3.3.27. Spectral Plot


Spectral Plot for Random Walk Data  
Conclusions 
We can make the following conclusions from the above plot of
the FLICKER.DAT data set.


Discussion  This spectral plot starts with a dominant peak near zero and rapidly decays to zero. This is the spectral plot signature of a process with strong positive autocorrelation. Such processes are highly nonrandom in that there is high association between an observation and a succeeding observation. In short, if you know Y_{i} you can make a strong guess as to what Y_{i+1} will be.  
Recommended Next Step 
The next step would be to determine the parameters for the
autoregressive model:
Such estimation can be done by linear regression or by fitting a BoxJenkins autoregressive (AR) model. The residual standard deviation for this autoregressive model will be much smaller than the residual standard deviation for the default model
Then the system should be reexamined to find an explanation for the strong autocorrelation. Is it due to the
