I developed a deep CNN model, based on the architecture discussed in this paper, to generate predictions for time series data. My training data is shown in the figure below:

enter image description here

In order to train the model, I tested two approaches. The first one where I normalize the data between 0 and 1, and in the second one I standardize the data based on the approach discussed here.

I noticed that in my case, standardization performs far far better than normalization (which gave me a practically flat line as predictions) for generating the predictions. I was wondering if there is any usually suspected general reason for why this could be the case, or is it just that one method works well for a particular type of problem?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.