Hello Denis,

(1) I appreciate your feedback, however, I feel I have all the right to ask a specific question related R namely what's the interpretation of the acf function plot. I gave away the information that it is a homework because many times people before helping ask what's the context for the question at hand.  If I don't understand something I will for sure ask. I don't have anything to hide so I don't care if there are professors subscribed to this list. My ultimate goal is to learn and it doesn't really matter whether it is studying a book, asking an assistant or asking in a forum. 

(2) After looking in many references and not finding any clue ... I Googled for information and found that I should be "looking for cyclic patterns" i.e. oscillations? There are none in this dataset so I presume there would not be any autocorrelation, oder?

(3) This is something very unfortunate ... the course Lectures are great, the course script is very comprehensive, however, the assignments many times include questions that are a bit off topic like in this case of Time Series and includes no actual reference ... so it is no surprise that even after diligently attending all lectures and doing all exercises I get stuck. Please recommend what's the best book in this topic of Time Series analysis maybe with R. I will buy it.

(4) Yes they mentioned something like this in the assignment "Dependency can be verified by showing that under the model, Cov(X_t^2,X_{t-h}^2) \neq 0, h > 0 (complicated). Plot and interpret the autocorrelation functions of X_t and X_t^2 for the BMW-dataset." http://stat.ethz.ch/teaching/lectures/FS_2010/CompStat/series4.pdf

Thank you.
Best regards,
Giovanni

On Apr 17, 2010, at 7:25 PM, Dennis Murphy wrote:

> Hi:
> 
> (1) If you read the posting guide (as you were asked to do before posting), you would find out
>       rather quickly that this is not a forum to help you with homework. Moreover, several of your
>       professors may subscribe to this list and notice your request.
> (2) What 'trend' is in this data set? It has excessive variation at certain points of the series,
>       but what trend?
> (3) None of your cited references is likely to have much that describes what autocorrelation means.
>      (The only exception might be HSAUR, but it focuses more on the programming than the concepts.)
>       I'd consult an actual time series text to learn the concepts you need to make sense of the plots.
> (4) You can't 'prove' that the series in question is (or is not) autocorrelated with R or any other
>       software; however, it can be used to provide empirical support for one hypothesis or the other.
>       Proof is a mathematical construct involving deductive logic, whereas statistical inference uses 
>       inductive logic. They represent different approaches to problem solving.
> 
> HTH,
> Dennis

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