I would like to combine to measurement tables to do some further mining activities. Unfortunately both tables use a different time steps (table 1: 1s, table 2: 0.1s) with an unknown time shift but with one common signal (a temperature signal).
What I am doing right now is to create an Excel File with the time and temperature from both columns. Create a scatter plot of both signals and add an additional time shift manually, so both signals match perfect (lay on each other).
Is there any way to do this automatically? In Knime?
I see. It can be done. In example you provided you just need to count number of rows with data value less then zero.
Just couple of questions. You know that time steps are always 1 sec for table one and 0,1 sec for table two? Does table one always start with data 0 are offset needs to be calculated for table two?
So I have read your data into Knime. Filter values less then 0. Extracted dimensions and used it in Column Expressions node to get shifted time.
in my case the time stepping is always 0.1s and 1s for both data sets. And yes one table always starts with 0.
I cannot download your example, so I think that my example was probably misleading.
The time shift between both signals is unknown. (Here in my example the result would be -10s).
The data (temperature measurement in real case) has no zero crossing or similiar.
I thought for illustration it would be convinient to work with a sinus signal
In Matlab I would do something like this:
pick “1s” time vector as main time
interpolate measurement values from 0.1s stream onto main time vector
assuming 0s as offset
calculate Root Mean Square Error of “interpolated_values - measured_1s_values”
repeat with changing offsets until RMS is under a certain limit
So I think you have to use some (maybe linear equation) solving of this problem.
one minor step ahead… Just found out, that there are some optimization nodes available. So I “just” have to add the interpolation and mathematics nodes… Just ehm… Well this does not work.
Hi Ivan,
yeah I managed it… with Matlab. It was the fasted solution for me. The interpolation issue from my last post I solved in Knime but the whole workflow was not very efficient. This was based on the optimization node, which does a constant stepping only. So if you start pretty far away from the solution it takes hours…
I recently dicovered the recursive loop nodes. Maybe I can create a Newton Solver with variable step size.
