input transformation in backpropagation neural network (prediction task)

Question

coqui 2015-11-16

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/255203-input-transformation-in-backpropagation-neural-network-prediction-task

回答： Greg Heath 2015-11-17

I have used historical prices to predict future price based on backpropagation neural network (fitnet). I have obtained a mse eaquals to 1.27. but when I transformed prices into returns I found a mse=5.266.

in this case, I must maintain the historical prices and no transformed it to returns???

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Greg Heath 2015-11-17

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/255203-input-transformation-in-backpropagation-neural-network-prediction-task#answer_200099

在 MATLAB Online 中打开

Regardless of the original target transformations, I suggest that you normalize MSE by the average target variance MSE00. The latter is the MSE that would result if your output model is as naïve as possible: a constant y = y00 that cannot model target variations. If you minimize the resulting MSE you will find

MSE = MSE00 = mean(var(target',1))

For any reasonable model that attempts to model target variations

0 <= MSE <= MSE00

In terms of the normalized MSE, NMSE:

         NMSE = MSE/MSE00
         0 <= NMSE <= 1

In terms of the coefficient of determination, Rsquare,

https://en.wikipedia.org/wiki/Coefficient_of_determination

          Rsq = 1 - NMSE
           0 <= Rsq <= 1

This is interpreted as the fraction of target variance that is "explained" by the model.

 I typically use a goal of 0.99 for regression and classification. 
 For timeseries I use a goal of 0.995 for open-loop designs.

Details can be found in zillions of my posts.

Bottom line: compare the NMSE or Rsq of the two models.

Beware that the best models for each may have different number of hidden nodes and different initial random training weights. Therefore, in each case you will have to find the best of multiple designs.

Hope this helps.

Thank you for formally accepting my answer

Greg