I have a lot of time series containing Data about the position of the biologic cell (X, Y).
What I would like to do is to remove all noise from the time series or at least a big part of it. I don’t know which kind of noise (model) is present and I can’t predict the X, Y movement.
Which method can I use on KNIME? What do you suggest?
it’s an interesting question. I don’t know if there is an existing approach for doing this, however, I think after succeeding defining a distance there should be an idea.
So I would try the following…
- get the previous values of the specific potential noise column by using the lag-node (-1 and +1,…)
- define a distance between the current value and value(-1)…
- calculate a measure like distance(-1;0)/distance(-1;1) [just a simple one]
- use numeric outlier node to detect noise / outliers
just sketching a possible workflow.
It would be useful to invest some statistical know-how in step 3.
No solution, just starting the discussion