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normalising and reverse normalising data

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NN
NN on 8 Dec 2020
Answered: Karan Nandankar on 28 Dec 2020
For forecasting , I normalised the data before training for test and train data , then when i plot the rmse , i get rmse 0.2.but when i try to reverse the normalised values to match with orginal target of test data it shows high error.
i have used below code to normalise and denormalise :
[pn,ps] = mapminmax(XTrain);
[tn,ts] = mapminmax(YTrain);
[qn,qs] = mapminmax(XTest);
[rn,rs] = mapminmax(YTest);
net = trainNetwork(pn,tn,layers,options);
YPred=predict(net,qn);
YPred1 = mapminmax('reverse',YPred,ts);
what could be the reason ?
  3 Comments
Star Strider
Star Strider on 8 Dec 2020
Reference: My Comment to your previous Comment.

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Answers (1)

Karan Nandankar
Karan Nandankar on 28 Dec 2020
Hi,
Looks like you have used the wrong Process Settings in the variable 'YPred1'. As I can see you are using XTest as your independent variable for Model Prediction, and the corresponding dependent variable YTest is normalized with Process Setting parameter 'rs'. However, for the variable YPred1 you have mapped the predictions against 'ts' (which is for YTrain).
In order to reverse the normalization, you can change the Process Setting parameter in YPred1 from 'ts' to 'rs'.
YPred1 = mapminmax('reverse',YPred,rs);
You can refer to this documentation for more information.

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