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dependent attribute
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Telcontar120 RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635 Unicorn@SimonK I understand, but don't get hung up on the vocabulary here. In this context "predict" is simply a word that means "represent as a functional combination of other attributes" even when it is already known. If your target attribute is actually a pure functional output of the other inputs, then your "model" will achieve 100% accuracy. If it does not, then you may not have a set of relationships that can be expressed with 100% consistency. Without seeing the dataset it is impossible to be more specific.
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Telcontar120 RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635 UnicornYes, of course, you simply need to filter for each series (whatever that means for your dataset, I guess it could be a single row or a group of related rows) and run the analysis for each one separately and store each resulting model using the Store attribute.
If you have a large number of series, this could be further automated with several of the Loop attributes, you might want to check those out and perhaps some of the tutorials regarding process control in RapidMiner.0
Answers
With the Generate Attributes operator you can create new attributes using several functions and other attributes from your dataset.
Regards
Lindon Ventures
Data Science Consulting from Certified RapidMiner Experts
With your tips, I get a general function that is calculated from my entire data set.
However, I would be interested to know whether it is possible to generate a separate function in each time series.