Data Science Asked by ShadowEagle on December 10, 2020
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()
model.add(Dense(100, activation="relu", input_dim=n_timesteps))
model.add(Dense(100, activation="relu"))
model.add(Dense(1, activation="sigmoid"))
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.fit(x_train, y_train, epochs=100, validation_data=(x_test, y_test))
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