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Nyanraltotlapun t1_isefd1c wrote

Hi. I have time-series data. I try to do all sorts of thing with it, forecasting and classification with RNNs and Fully Connected models.

The question is - can neural networks capture speed of change of values? RNNs and FC ones? Should I try to feed networks with derivatives of my values? Or it can potentially worsen performance of my networks?

Second question, how should I normalize derivative, my first idea is to take absolute values of derivatives and encode sign as separate features(two features for positive and negative). Does it sounds reasonable? I am afraid of my data becoming to complex.

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neuroguy123 t1_isjnunn wrote

I think you're overthinking it. Maybe try encoding your data as the vector change from one point to the next if you want to help the network learn about relative changes. See https://arxiv.org/pdf/1308.0850.pdf

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Nyanraltotlapun t1_iso6lqp wrote

For example I encoded it as such. Different features have different scales and I need to normalize it somehow. But because differential encoding produce signet values I have problem with it. I afraid that with normalization I will lost information about direction(sign)

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