I have a data.frame which has multiple time series in it, in the following manner:

01.01.16 Value1-Time-Series-1 Value2-Time-Series-1 Value3-Time-Series-1 ID-1
01.02.16 Value1-Time-Series-1 Value2-Time-Series-1 Value3-Time-Series-1 ID-1
01.03.16 Value1-Time-Series-1 Value2-Time-Series-1 Value3-Time-Series-1 ID-1
01.01.16 Value1-Time-Series-2 Value2-Time-Series-2 Value3-Time-Series-2 ID-2
01.02.16 Value1-Time-Series-2 Value2-Time-Series-2 Value3-Time-Series-2 ID-2
01.03.16 Value1-Time-Series-2 Value2-Time-Series-2 Value3-Time-Series-2 ID-2


  • There is one timeframe which is the same for every time series
  • There are multiple (thousands) of time series within that time
  • All are made up by the same type of values (columns)

I want to cluster this dataset in order to find the time series objects similar to each other. I want to find those time series(s) which changed the same with respect to the values mentioned. I'm obviously interested in the measured distance. But using a simple hierarchical clustering algorithm is not cutting it since I need to tell the (whatever clustering algorithm I end up using) what is a time series and what not.

Any ideas how to this?

I'm using R for this. If there might be a better option with Python, I'm totally open to use it.

Thanks for any kind of help!


1 Answer 1


For an applied solution to your problem, I highly recommend reading the following: TSclust: An R Package for Time Series Clustering which can be found here. It presents the TSclust package in R and provides code.

Note that time series data is special, and cannot be treated like other data. So typical clustering techniques are not appropriate.


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