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I've completed this tutorial on how to implement an LSTM in dataiku.
Basically I want to understand how to adapt this for multiple variables.
I have a data set that has many 'runs' of a chemical process over a 10 day period, and different observations of the process over those 10 days like pH, temperature, etc. I want to use an LSTM that will predict the observations of the next day given a a few of the previous days.
I can't find anything like this or any examples of any multivariate LSTMs implemented in dataiku. Do you know of any tutorials that could help?
I have figured it out after much effort. The gist of the changes are
I have figured it out after much effort. The gist of the changes are
Thank you for sharing your solution with the rest of the community @Darius679!
Thanks for sharing this. Could you maybe share the snippets of the changes that needs to be made to windowprocessor.py and Architecture to run multivariate LSTM?