LSTM-Neural-Network-for-Time-Series-Prediction
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jaungiers
Code for De-Normalization
Can someone write code for De-Normalization so I can plot predictions on real data values
@alexbrillant We here have the demonstration of a good use-case for a NormalizationWrapper MetaStep object.
We should code a NormalizationWrapper (MetaStep) in Neuraxle that normalizes the data before sending it to a wrapped pipeline step, and that then denormalizes it before returning the results.
Inside that, the same normalization could be applied to y from the normalization values learned from x such that the wrapped neural network just sees normalized data and also predicts normalized data.
The same concept could go with a LogWrapper that would take the LogPlusOne of the values and also undo the transformation after.
Even further into this thinking, we could have a step that instead has a principal wrapped step, and also a list (or pipeline) of other steps to "apply" as preprocessing and then "unapply" as a reverse transformation after the processing is done to properly wrap the neural network. Let's call this a ReversiblePreprocessingWrapper.
Actually, I opened an issue here to do that, as we'll need it anyways: https://github.com/Neuraxio/Neuraxle/issues/59
Can someone write code for De-Normalization so I can plot predictions on real data values
the denormalization formula is given onthe altumingelligence article.... i could post the code here to do it in place where the normalization is done if you like, but it it very straightforward
sure to do a reverse check - ie denormalisation, inside the normalisation function, do this
denormalized_col = [ ( float(window[0, col_i]) * (float(n) + 1 ) ) for n in normalised_col ]
(denormalisation func - Pi = Po*(Ni + 1) (from the article text)
I don't think it is possible to denormalise the predicted data with that formula. It will results in some values very close to 0. I suggest to change the normalisation to use MinMaxScaler from sklearn instead. You can denormalise using the invert function.
I don't think it is possible to denormalise the predicted data with that formula. It will results in some values very close to 0. I suggest to change the normalisation to use MinMaxScaler from sklearn instead. You can denormalise using the invert function.
could you write this MinMaxScaler code here？thank you！！
