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…thon There were minor differences between the implementation, the difference was actually due to the default setting of noramlisation of the response. It is defaulted to True in R, while False for Python. Normalisation usually improves the fit and prediction, this is also supported by the examination of the prediction. Thus, we have adjusted both to standardise the response
Note, the RNN implementation is very crude, and requires further finetuning
1. We extended the input data by the timestep size, so the prediction starts on the same day as other models. 2. the forecast input is padded so that the forecast give the right length of output.
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This was due to an incorrect PR from a branch directly to
master.