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Time Series using Windowing operator in RapidMiner
I'm trying to use a time series model in RapidMiner to forecast premium paid to an insurance company. Specifically, I have an entry for each month from January 2009 - December 2015, I want to be able to forecast the data for the next 12 months (January 2016-December 2016).
I'm having trouble understanding how the Windowing operator works, I have a few questions:
1) What goes into selecting a window size? If I want to forecast Premium over the next 12 months, is my window size 12? And if so, why do I get 12 attributes for each original attribute in my data set (the original Premium amount in one of these 12)? I get that this is supposed to explain the corresponding label value (which is just the next row's original Premium, not sure why this is happening either), but where are these numbers coming from and why does RapidMiner generate these?
2) What does the option "create single attributes" do?
3) The horizon field: If this is the distance between the last window value and the value to predict, does this mean I can't at once predict the next 12 months of data? Even if I enter the horizon as 1 (which I take to mean, give me the prediction for January 2016 since the last data point is for December 2015), then why is there no label value for December 2015 or January 2016 in the output when I run the process?
I'm a beginner, and I would really appreciate any help!