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You missed the point. This has to do with "time series data" which means time is the same among all the data sets. Because you know this you should remove it to
by nemo44x 12y ago
You missed the point. This has to do with "time series data" which means time is the same among all the data sets. Because you know this you should remove it to test for similarity. You could apply this to any automatic variable in your domain but time & change is very obviously a typical common variable for many data sets.
- nkurz 12y agoPlease help me understand. Assume I have 100 years of atmospheric CO2 levels and 100 years of temperature data over the same span. These are both "time series data". What are you suggesting that I remove before testing for similarity? Does this help me to determine the degree to which one of the two variables is causing the other?
- nemo44x 12y agoI think time - then you're left with data. If when removing this they still have the same shape they do in fact correlate. I'd be curious to see the results of that experiment to be sure. I believe the beauty is you're using time (x and x-1) to preserve the change from sample to sample but remove the time influence that broadly affects all and distorts the subtle differences that may in fact not be related. It only matters if the commonality is far more influential and unimportant than the unique but import parts.
- cbsmith 12y agoYou should first break out the relationship between CO2 and time, and the relationship between temperature data and time. You effectively normalize both for time. So, in effect, you model the correlation between CO2/time vs. temperature/time. The interesting correlation would be if every time CO2/time deviates you can find a correlation with a temperature/time deviation.