# Binary classification of multiple Sequences using Keras

I am trying to classify multiple independent sequences using Keras. My data looks like this (example with different stocks and their values).

  _stock     2010   2011   2012   2013   2014
----------- ------ ------ ------ ------ ------
foo          100    200    250    300    400
bar           50    100    100     50     25
pear         100    250    250    300    400
raspberry    100    200    300    400    500
banana        50     20     10     10      5


I would like to classify the data like shown in the following structure. The labels are already pre-defined for each stock (supervised learning).

  _stock          label
----------- -----------------
foo         0 (not falling)
bar         1 (falling)
pear        0 (not falling)
raspberry   0 (not falling)
banana      1 (falling)


Finally, I would also like to predict the next timestep, if possible.

  _stock     2015
----------- ------
foo          450
bar           10
pear         500
raspberry    600
banana         1


Currently I'm just using a bunch of Dense Layers which is working fine, but I don't think that I'm not utilizing the relationship between each column in the right way with this setup. Furthermore I don't think that a prediction is possible with this setup.

# current network
from keras.models import Sequential
n_timesteps = len(data.columns)

model = Sequential()