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!