Modeling Long and ShortTerm Temporal Patterns
Dear Rapidminer Community,
I recently read about Multivariate Time Series Forecasting with LSTMs and tried to rebuild something similar in Rapidminer but I understood that this is only possible when using the Keras Extension? Is this correct? Or is there a way how to do this in Rapidminer without installing and setting up the Keras and Tensor environment?
In general, are there plans to integrate an operator set to quickly deal with LSTMs directly in Rapidminer Studio? Or is there already one and I totally missed it?
Best regards
Felix
My sources:
https://machinelearningmastery.com/multivariatetimeseriesforecastinglstmskeras/
https://arxiv.org/pdf/1703.07015.pdf
I recently read about Multivariate Time Series Forecasting with LSTMs and tried to rebuild something similar in Rapidminer but I understood that this is only possible when using the Keras Extension? Is this correct? Or is there a way how to do this in Rapidminer without installing and setting up the Keras and Tensor environment?
In general, are there plans to integrate an operator set to quickly deal with LSTMs directly in Rapidminer Studio? Or is there already one and I totally missed it?
Best regards
Felix
My sources:
https://machinelearningmastery.com/multivariatetimeseriesforecastinglstmskeras/
https://arxiv.org/pdf/1703.07015.pdf
Tagged:
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Best Answer

pschlunder Posts: 71 RM ResearchHi felix_w,look out for tomorrows update. Recurrent layer handling (and for now LSTM layers first) will be added to the extension. Then you'll be able to perform multivariate timeseries forecasting with the DL ext. as well without having to rely on Keras.It will also include some sample processes.Btw: Also included will be an operator to load models trained in Keras and apply them in RapidMiner without having to install keras.Regards,Philipp10
Answers
thank you very much for your reply!
Any chance that there will be "recurrent layers" implemented in the Deep Learning extension in the near future?
Best regards
Felix
Exactly what I needed!
Thank you!
Thanks,
Varun
Varun
Regards,
Varun
Varun
1st: the problem domain: My input data is financial OHLC so 4 attributes/features.
Additional assumption: Window size in the Window operator = 10, so 10 timesteps.
I want to do classification, not prediction. Therefore I don't generate a label in the Windowing operator. If you do generate a label in the Window operator, or you use another amount of timesteps, horizon, etc. you have to adjust the parameters below accordingly.
So in my application his results into an Keras operator input example set off 'n' records with 4*10 = 40 normal (windowed) attributes and a label. The Keras operator input shape is "(40,)" including parentheses and the comma.
The Add Core Layer Reshape operator, that you add as the first operator within the Keras operator, should get a target shape of "(10,4)".
This is the point where I suspect everybody goes wrong: Yes, we all use RNN but not as the FIRST operator in the Keras operator but it's the SECOND operator. Therefore output shape consist out of 2 parameters, not 3 (which generates an error).
Hope this helps.
I've been trying to reproduce your solution but I can't make it work. I started from your initial XML sample, generated fake data to fit what the example expects, and then tried the modifications in your last post. I guess you did additional modifications to the original XML to make it work. Would you be able to provide your final solution?
Thanks