<p>I found this <a href="https://arxiv.org/abs/1510.01378" rel="nofollow noreferrer">https://arxiv.org/abs/1510.01378</a> 
If you normalize it may improve convergence so you will get lower training times.</p>
Should I normalize my features before throwing them into RNN?
<p>I am playing some demos about recurrent neural network. </p>

<p>I noticed that the scale of my data in each column differs a lot. So I am considering to do some preprocess work before I throw data batches into my RNN. The close column is the target I want to predict in the future.</p>

<pre><code>     open   high    low     volume  price_change  p_change     ma5    ma10  \
0  20.64  20.64  20.37  163623.62         -0.08     -0.39  20.772  20.721
1  20.92  20.92  20.60  218505.95         -0.30     -1.43  20.780  20.718
2  21.00  21.15  20.72  269101.41         -0.08     -0.38  20.812  20.755
3  20.70  21.57  20.70  645855.38          0.32      1.55  20.782  20.788
4  20.60  20.70  20.20  458860.16          0.10      0.48  20.694  20.806

     ma20      v_ma5     v_ma10     v_ma20  close
0  20.954  351189.30  388345.91  394078.37  20.56
1  20.990  373384.46  403747.59  411728.38  20.64
2  21.022  392464.55  405000.55  426124.42  20.94
3  21.054  445386.85  403945.59  473166.37  21.02
4  21.038  486615.13  378825.52  461835.35  20.70
</code></pre>

<p>My question is, is preprocessing the data with, say StandardScaler in sklearn necessary in my case? And why?</p>

<p>(You are welcome to edit my question)</p>