Modeling Long- and Short-Term Temporal Patterns

felix_wfelix_w Member Posts: 59  Maven
edited December 2018 in Help
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/multivariate-time-series-forecasting-lstms-keras/
https://arxiv.org/pdf/1703.07015.pdf
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Answers

  • hughesfleming68hughesfleming68 Member Posts: 114   Unicorn
    edited December 2018
    To be honest, the best way to use Keras in Rapidminer is to first learn how to use it in Python. It is not as straight forward as some of the other extensions. There are no out of the box LSTM operators in Rapidminer but you can do time series forecasting and I get good results from the built in operators. There is also the Deeplearning4j extension that is worth looking at. Learning to use the built in operators will also give you a point of reference to see if LSTM's work well on your data. I have had mixed results.

    I have bought Jason Brownlee's E-books on Deep learning and time series forecasting. They are helpful for getting up to speed with Keras.
    regards,
    Alex
    felix_w
  • felix_wfelix_w Member Posts: 59  Maven
    Hi Alex, 

    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
  • felix_wfelix_w Member Posts: 59  Maven
    @pschlunder Wow, perfect :) :)

    Exactly what I needed! 

    Thank you! 
    mschmitzpschlunder
  • varunm1varunm1 Member Posts: 63   Unicorn
    edited December 2018
    @hughesfleming68 One big issue using keras in RM is that it doesn't give clear error details. I am working a lot on applying deep learning in python but when I tried to shift to RM with new Keras extension its throwing different errors. I even posted it in the Keras thread, unfortunately, no response from anyone. The same network, when applied in python, is giving the output. Also, is this keras extension tested with cross-validation (CV) operator? @pschlunder.

    Thanks,
    Varun
  • jczogallajczogalla Employee, Member Posts: 92   RM Engineering
    Hi @varunm1, the Keras extension might have problems because you might need to specify the different input/output shapes. @pschlunder is not responsible for the Keras extension however (neother am I), so I can not give you more pointers there, sorry.
    The new Deep Learning extension however does not use Python, but DL4J, a Java-native deep learning library. You can create the models with the well-known RapidMiner operator concept, but you can now also load keras models that you built in Python and apply them to RapidMiner example sets. That should also give you more comprehensive error messages if an error occurs.
    mschmitzhughesfleming68varunm1
  • hughesfleming68hughesfleming68 Member Posts: 114   Unicorn
    edited December 2018
    @varunm1. I had similar issues and just continued with Keras in Python.You might want to try the new Deeplearning extension that @pschlunder just released. It is based on Deeplearning4J. I have just started to go through the examples. It is probably a good idea to start a new thread just for Deeplearning as it is evolving very quickly.
    mschmitzvarunm1jczogalla
  • varunm1varunm1 Member Posts: 63   Unicorn
    @jczogalla @hughesfleming68 @pschlunder Great. Thanks guys. Will work on the new release of Deep Learning extension.

    Regards,
    Varun
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