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I've started to work on time series. I was wondering what would be the best data normalizing and pre-processing technique for non-linear models, specifically, neural networks.

One I can think of is min-max normalization

$$z = \frac{x - min(x)}{max(x) - min(x)}$$

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  • $\begingroup$ are we assuming that the min and max do not change over time? specifically, from the training data to the test data. If there is trending or drift then z could end up negative or above 1 for large portions of test data using this normalisation method. $\endgroup$ – Mike NZ Apr 19 at 6:20
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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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