1
$\begingroup$

I would like to classify a relatively large set (over 9000) of short times series. The length of each sequence varies, but I would say about 80 % has between 2 and 9 observations. While I could use a simple trendline (maybe combined with a variance measure) to describe each these sequences, I would like to go a step beyond this solution.

What other kinds of methods could I utilize to cluster the "visual appearance" of these sequences? The ultimate goal of the classification is to gain a understanding of what type of trend/style/behavior each sequence is exhibiting.

$\endgroup$
  • 1
    $\begingroup$ The fact that the series differ in length greatly (greatly because they are short) will be a stumbling-block for any attempt of classification, even just visual one. $\endgroup$ – ttnphns Mar 12 '12 at 15:28
  • 1
    $\begingroup$ Classify as in supervised learning a map to a given target class label? Or did you mean clustering? $\endgroup$ – Memming Mar 12 '12 at 15:38
  • $\begingroup$ Did manage to use the wrong wording there: what I want to do is cluster. $\endgroup$ – Figaro Mar 12 '12 at 16:45
  • $\begingroup$ Try running them through OPTICS with an appropriate dsitance function, e.g. DTW. $\endgroup$ – Has QUIT--Anony-Mousse Mar 12 '12 at 17:28
1
$\begingroup$

I can give you few hints:

  1. You could use dynamic time warping to extract similarity between your sequences. Please see : Can someone please explain dynamic time warping for determining time series similarity?
  2. Cave plot (visualization as you have stated) is another option. http://cm.bell-labs.com/cm/ms/departments/sia/project/misc/caveplot.html
  3. If you use R, http://www.rdatamining.com/examples/ts-mining may give you some hint
  4. You may want to see a discussion on similar topic at Time series 'clustering' in R
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.