7
$\begingroup$

I am interested in clustering multivariate N time series of T'values' each(different lengths) using python. Each variable have many trends and values which are simultaneously numeric and nominal.

A sample $T_{i}$ in the dataset has the following format:

TimeStamp       | Sensor0 | Sensor1| Sensor2
2015-02-05 11:30|<Min     | On     | off
2015-02-05 11:31|<Min     | on     | off 
2015-02-05 11:32| Action2 | 10     | 0.0001  
2015-02-07 11:33| Action2 | 10     | 0.00012 
2015-02-07 11:34| Action2 | 10     | 0.00012 
2015-02-07 11:35| Action2 | 20     | 0.00015

Another sample $T_{j}$ in the dataset has the following format:

TimeStamp       | Sensor0 | Sensor1| Sensor2
2015-10-05 11:30| Action2 | 11     | off
2015-10-05 11:31| Action1 | 11     | off 
2015-10-05 11:32| Action2 | NAN    | 0.0001  
2015-10-07 11:33| Action3 | NAN    | 0.00012 
2015-10-07 11:34| <Min    | 10     | 0.00012 
2015-10-07 11:35| <Min    | 15     | on

For the missing values (not numeric), they were not collected by the sensors so my idea was to replace them by minimum values., given that all values are strictly positive. Otherwise, they would be considered as missing values. In which case the problem would be of finding a similiraty measure that can compare missing values (off,on..) and numeric values.

I am wondering if there is a similarity / distance measure already exist in the litterature to compare such multivariate timeseries, with hetergonuos lengths, and whether this kind of problem has already been formulated in the papers, books or else for R and python.

Thanks for your advice.

Improve this question
$\endgroup$
7
  • $\begingroup$ Fit the time series to a model, and cluster the model parameters. $\endgroup$
    – Emre
    Commented Aug 16, 2016 at 19:45
  • $\begingroup$ @ emre thank you for your response. I just can't seem to find a way of finding the right modeling framework for such context. So you have a specific method in mind? $\endgroup$
    – user23440
    Commented Aug 16, 2016 at 21:48
  • $\begingroup$ I'd use a neural network. $\endgroup$
    – Emre
    Commented Aug 16, 2016 at 21:51
  • $\begingroup$ That's indeed a very good paper. I am a newbie to deep learning. It will be difficult for me to implement it in python. Do you know of good resources for a beginner(books moods github..)? $\endgroup$
    – user23440
    Commented Aug 16, 2016 at 22:24
  • $\begingroup$ Start here, then try these time series tutorials. $\endgroup$
    – Emre
    Commented Aug 16, 2016 at 23:03

1 Answer 1

Reset to default
2
$\begingroup$

Try this recent paper: Consistent Algorithms for Clustering Time Series.

Your question is very much a current research topic.

Here's an older but excellent paper which talks about the fundamentals: Generalized Feature Extraction for Structural Pattern Recognition in Time-series Data.

Improve this answer
$\endgroup$
1
  • $\begingroup$ @ Pete thank you for your response.this seems to me an interesting avenue to explore.I will get back to you when I finish reading $\endgroup$
    – user23440
    Commented Aug 16, 2016 at 21:49

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

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