Hello,
today I was playing around with the new nodes in KNIME 2.11.
- time series missing value
- seasonality correction
I did not really understand it while studying the example 050010_Energy_Usage_Time_Series_Prediction
Here is my data and what I want to achive. I collect energy data in irregular time intervals maximum every 5 minutes.
row ID date-time-stamp energy Row325143 2013-12-21T17:37:20 783,66 Row325201 2013-12-21T17:42:20 783,75 Row325259 2013-12-21T17:47:20 783,95 Row325317 2013-12-21T17:52:20 784,13 Row325375 2013-12-21T17:57:20 784,32 Row325443 2013-12-21T18:02:46 784,54 Row325501 2013-12-21T18:07:20 784,83 Row325559 2013-12-21T18:12:20 785,06 Row325617 2013-12-21T18:17:20 785,20 Row325675 2013-12-21T18:22:20 785,24
The problem with this is that one cannot use all the nice prediction and mining functions from the energy examples available here as date-time-stamp is very uneven. So I have to make very regular date-time-stamp data out of this. I tried to do this with the missing value node, but could not find the right settings.
This is my expected result: Resample all data points to 15 Min intervals with linear interpolated energy values and a delta caluclation in the last colum:
row ID date-time-stamp energy regular-date-time resampled-energy delta Row325143 2013-12-21T17:37:20 783,66 Row325201 2013-12-21T17:42:20 783,75 Row325259 2013-12-21T17:47:20 783,95 2013-12-21T17:45:00 783,86 0,57 Row325317 2013-12-21T17:52:20 784,13 Row325375 2013-12-21T17:57:20 784,32 Row325443 2013-12-21T18:02:46 784,54 2013-12-21T18:00:00 784,43 0,71 Row325501 2013-12-21T18:07:20 784,83 Row325559 2013-12-21T18:12:20 785,06 Row325617 2013-12-21T18:17:20 785,20 2013-12-21T18:15:00 785,13 Row325675 2013-12-21T18:22:20 785,24
From there I could continue with the big-data-time-series white paper...
Happy about any help