How to pad real-valued sequences I have several sequences of univariate real-valued time-series data. The sequences are of different lengths and right now I cannot batch them and feed them to a network.
What is the correct procedure to pad these sequences? Is it even possible in this case since I can't use any number as a special symbol?

UPDATE 1 

I'm working with arbitrary univariate time-series data (not related to one specific domain, unbounded range). To give example of one such a series consider standardized stock dataset (only first 10 elements shown):

d = array([-0.37807043, 0.14321786, -0.37807043, 0.13478392, 0.18733381,
   1.19576774, 0.25675156, 0.26064414, 0.30930144, 0.38650436])
How you pad it (and even whether you do so) would depend on what you expect of the data. This imposes boundary conditions on the data which will induce artifacts in any transform you make. How bad this effect depends on how well geared your data is to accepting a particular padding method.

Padding methods include zero padding or a periodic bound.

Padding doesn't have to be done in the time domain. Eg interpolating in the frequency domain and back transforming allows you to extrapolate.

If your analytics has a finite history (eg FIR filters) then you can isolate time regions where padding is unnecessary and draw comparisons therefrom.