How can I use deep learning with windowing operator when the horizon is larger than one?

If forecasting the S&P Index was easy then there would be a lot of rich data scientists :-)
You might want to look at the new Forecasting extension, which has some automated operators for both univariate and multivariate forecasting.

Hi @hsanchez

I agree with Brian and I can not prevent me to quote Pierre DAC : 

"...Forecasting is difficult, especially when it comes to the future..." 

Regards,

Lionel

I see your problem, the issue is that currently RM Deep Learning (built in and extensions) do not support returning results as tensors, i. e. multiple labels or their vectors. This means that in Deep Learning forecasting you are limited to the horizon of one, this is regardless of what algorithm you use CNN or LSTM. However, as @Telcontar120 suggested RM features multi-horizon operators, both in the new Forecasting extension and in Time Series extension, which could solve your problem using the classical forecasting algorithms. 

Another possibility is to use a Python extension, prepare your data in RM, do all your Deep Learning magic in Python with Tensorflow (for example) and then return the multiple horizon output as a vector back to RM. 

Hello Guys, Thank you very much for taking your time to look at my post and answering my question with good humor @Telcontar120 and some comments from @lionelderkrikor and thanks to @jacobcybulski for his answer. I would say that @jacobcybulski answered my question. Yes, sure Stock Market prediction is a difficult thing but that does not prevent us to try it and challenging ourselves with something very close to "can we forecast emotions of a group of people?". Each stock, groups people by a common interest and behavior. I may say each stock represents lets say an average emotion corresponding to the people forming  that stock. Yes, sure it is chaotic/random but should be a way to get it right "some times". I am not market specialist, I am just curious and I never read a book about stock market. Having the curiosity as driver and rapidminer as tool off hope  I decided to do something that yield lets say by luck 400 dollars. Yes, that will not make you rich @Telcontar120 but I was able to buy tons of satisfaction.  By the way, I used GBT and I would say that algorithm is a rock!!! it is wonderful. 
Pull the intraday data using Python->Apply STL to remove some noise->(I am using multi-variable), normalize series->weight by PCA to focus on those variables that real matter-> windowing (to train, validate, and use my last row to apply my model) and another parallel windowing to enrich the data (feature generation) with parameters such as min, max,  std deviation, etc. Split the data in three parts: train, evaluate the model, and use the last row as unseen, multi-horizon forecasting using GBT (GBT wow! when you tune it), multi-horizon performance. Apply model to the unseen data, multi-horizon performance, Tune ARIMA and apply it to the sequence to compare its performance/forecasting  with that designed using GBT. 
Then you  can get some satisfactions when "some times" you get it right. 
What I learn? 

1. Rapidminer did a great job with this time series extension.
2. Each stock has its own emotion and behavior.
3. There is not such thing of "free lunch" , you shall develop one model per stock. Each stock has its own personality and emotions. 
4. GBT well tuned can surprise you.

I want to remark. I am not an expert in time series either stock market. I am just curious and the process described above may be subject to missing steps. 
Thank you guys for your time