normalising and reverse normalising data

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NN
NN 2020-12-8
回答： Karan Nandankar 2020-12-28
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 ?
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NN
NN 2020-12-8
i am using 2019b version.
i am doing forecasting Problem,
So i applied mapminmax function to rescale the data .
I referred below matlab documentation.
https://www.mathworks.com/help/deeplearning/ref/mapminmax.html
i am getting minimum rmse value with normalised output and test data .But when i reverse it to original scale , it shows different rmse..
Why is it happening .Kindly help..
i used below code:
[pn,ps] = mapminmax(XTrain);
[tn,ts] = mapminmax(YTrain);
net = trainNetwork(pn,tn,layers,options);
qn = mapminmax('apply', XTest,ps);
[rn,rs] = mapminmax(YTest);
YPred=predict(net,qn);
YPred1 = mapminmax('reverse',YPred,ts);
figure
plot(YPred1)
hold on
plot(YTest,'.-')
hold off
legend(["Observed" "Predicted"])
ylabel("SolarIrradiance")
title("Forecast with Updates")
rmse = sqrt(mean((YPred1-YTest).^2))

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回答（1 个）

Karan Nandankar
Karan Nandankar 2020-12-28
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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