[R] Offline change detection methods?

Henrik Bengtsson hb at maths.lth.se
Mon Sep 10 17:43:30 CEST 2001

Dear [R] people, I am looking for a method to test if there are any
changes (jump points) in a time series data set or not. If there are, I
would like to identify the jump points. All the data is available from the
beginning so an offline method could be used.

The current data set that I have could be seen as a time series with
five almost equally sized segments that stands out to have different
(constant) means and possibly different variances. All 10000 data points
can be considered independent and containing 1-2 percent outliers.
Actually, the data set contains not only one variable, but several
variables that are all known to have the same jump point positions, if
any. I believe that several variables could be used in a multivariate
approach to improve the identification of the jump points, but a 
univariate method is ok too.

It would also be possible to identify the positions visually, but I am
looking for a method that could be applied automatically. It should 
ofcourse also be applicable to new data sets, which could have totally
different number of jump points, jump point positions, and different means
and variances. Between different data sets there is little or nothing in
common and for this reason, I believe, methods like HMM won't work
since I have no test data to learn the models. Also, even though the jump
points are within about the same distance from each other in the current
data set, this might not always be case. 

The current reference I have is "Detection of Abrupt Changes - Theory and
Application" by Basseville & Nikiforov. They explain several methods which
seems promising to me, even a few offline methods. Do anyone know about
other references? Do anyone know if B&N's methods (or others) are 
implemented in [R] or S-Plus? If not, maybe someone knows about other
implementations that I could port?

Thank you

Henrik Bengtsson

Dept. of Mathematical Statistics @ Centre for Mathematical Sciences 
Lund Institute of Technology/Lund University, Sweden (+2h UTC)
Office: P316, +46 46 222 9611 (phone), +46 46 222 4623 (fax)
hb at maths.lth.se, http://www.maths.lth.se/matstat/staff/hb/

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