You're going to need the process that Bala D wrote about in his book. Take a look at the last process in this thread. http://community.rapidminer.com/t5/RapidMiner-Studio-Forum/Recall-Error/m-p/37302#U37302
Hi Thomas,
I still have not reached a satisfactory learning point regarding Time Series Analysis and Prediction. I'm sorry.
I admire your blogs/videos and answers a lot but I still have questions:
Why do the "Windowing" and the "Sliding Window Validator" operators offer both parameters for Window, Step Size and Horizon?
How do they influence each other or cooperate/work together?
Are there any "rules of thumb" to implement the parameter sets of both operators in conjunction with each other?
To explain my question:
If the Windowing operator parameters are Window=1, Step=1, Horizon=1: What happens if the parameters of the adjacent Sliding Window Validator are configured as Window=5, Step=1, Horizon=1, like in your Time Series videos #9? Examining the output example set from the Windowing operator shows already a Label with training data for the predictions for time n+1 because of Horizon=1. Will the Sliding Window Validator create a new Label for its own Step=1? What do we get then, a label fit for a prediction for n=2, a cumulation of n=1 from Windowing Step=1 plus n=1 from the Sliding Window Validator Step=1? Apparently not but I don't understand it. Suppose both Windowing and Sliding Window Validator operators have Horizon = 5: What do we get then, a Label as input for the training of the algorithm for time n+25 or n+10? Again, I do not understand the end-to-end process.
If the answer is that the Windowing and the Sliding Window Validation operators, including their parameters, work completely independently of each other in the end-to-end process: Why would one train/validate an algorithm such as SVM with 5 times more features/attributes than the initial example set from the Windowing parameter? I guess that a Sliding Window Validation parameter Window=5 will result in an SVM hyperplane based on vectors in 5 dimensions. But these vectors point to "nowhere" compared to the vectors from the input example set coming from the Windowing operator with Window=1. Why not set the Window parameter of the Windowing operator then also to 5, so the dimensions of the vectors of the input example set match at least the dimensions of the trained/validated vectors of the SVM hyperplane?
You might understand: I'm feeling overtrained.
Please elaborate. Thanks.
Oh hey @luc_bartkowski I didn't see your questions. It's best to get my attention by using the '@' symbol and tagging me in the future.
So just to clarify, the Windowing operator is a transformational operator. It just transforms your time series into a multidimensional data set based on the parameters you use. The Sliding Window Validation operator is use to for training and testing your model for performance. Yes they have similiar parameters called Steps and Widths, it's just they're applied differently. Good luck!