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I would do the exact same thing as you are describing! One of the main reasons that you would want to do cross-validation is to prevent that your model is unable to generalize later. Therefore, you take out a random small subset which will be your new small validation set and do all the 'operations' on that which you are also doing on your training set. This ...


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I would say any normalization such as min-max or standard deviation is fine as far as the scaling factor is provided as a feature, since time-series of different scale might behave differently.


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This question has been asked a year ago when I faced this problem I searched and it hasn't any answers on it, I tried different ways and finally, data augmentation helped me. I used data augmentation and a very small learning rate. If the fluctuations are big, the batch size should be increased, and the learning rate should be decreased. After all, using ...


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There are different questions and even different lines of thought here. Let's go through them On resizing Why do we need to resize? To fit the network input which is fixed when nets are no Fully Convolutional Networks (FCN) What if my net is FCN? Still makes sense to resize to bound the dimension of the input features you want to detect (a person on a small ...


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