How to normalise daily trading volume and trading value as features for RNN model of stock time series?

The immediate answer could be: taking the global min/max, mean/std for each stock across the whole time series window, normalise the daily volume/value of the corresponding stock, stock by stock. However, during each stock cycle, the trading volume/value range in the particular cycle could vary drastically compared to other cycles, in all these “local” window of a given stock, does it make sense to find dynamic local min/max, mean/std for local normalisation? Or, does it make sense to calculate derivatives of volume/value, 1st and 2nd orders to capture and velocity and acceleration of the market capital flow?


1 Answer 1


You must not use "tomorrow"'s data to normalize "today"'s one. So it is not a good idea to use global min/max, mean/std ... across the whole time series window. The same true is for "cycles" - you can't use data from the end of the cycle to normalize data in the beginning of the cycle.

So, at every time point of your time series you may do any kind of normalization if you are using data which were known before that point.

It does make sense to use derivatives. The operations is usually called differencing in time series literature and is used to transform non-stationary series into a stationary one, for example. See, for example here p.76 "Differencing"


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