# Can RNN learn for each t in time from a whole new dataset (many entries)

Basically, my data set is not as simple multi-variate time-serie as it's often (to some extent) the case.

For each month, I have N entries (not less than 3000). Can RNN of any variant (Please bear my ignorance, as I am a newbie in deep learning) catch in memory what it learnt from i.th subset and proceeding with learning ...

Data is like:

+======================================================================================================================================================================================+
| index, target, feature1, feature2, feature3, feature4, feature5, feature6, feature7, feature8, feature9, feature10, feature11, feature12, feature13, feature14, feature15, feature16 |
+======================================================================================================================================================================================+
| 2013-01-01, 1, 12, 0.006750, 21.192372, 39.119279, 0, 0, 0, 0, 13.602740, 117691.0, 0.06, 17259.0, 61491.0, 10.960000, 44620.0,                                                      |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2013-01-01, 1, 12, 0.256899, 21.192372, 39.119279, 0, 0, 0, 0, 30.282192, 835.0, 0.06, 221.0, 344.0, 10.004412, 406.0,                                                               |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2013-01-01, 0, 12, 0.000500, 21.192372, 39.119279, 0, 0, 0, 0, 30.282192, 49292.0, 0.04, 10853.0, 22945.0, 10.004412, 20132.0,                                                       |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+


and the list continues for other months.

EDIT

My baby wail data-frame, with repetitive multivariate times-series is like

index        features...
2013-01-01
2013-01-01
2013-01-01
...
2013-02-01
2013-02-01
2013-02-01
...


and the list goes on with the same length for each month. As I want to learn from time along with other features, I reshaped the data-set with multi level indexing into subsets with the same length taking the next nth*iteration entries and spreading over subsets tails.

I got something like

multi_level_index   features...
1 2013-01-01
2013-02-01
2013-03-01
...
2 2013-01-01
2013-02-01
2013-03-01
...
...


Can I benefit all learning data and feed into LSTM Keras solution (or other, but preferably in Python)? I am following an approach I found here