Binary classification of multiple independent Sequences using Keras

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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 value at 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 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. I would like to use something like an an LSTM network, but I don't know how to change my implementation.

# 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))
keras prediction classification sequence python
2 Answers

This type of learning is called multi-task learning. You can have multiple outputs and multiple loss functions. To handle the sequential nature of the dataset you can still use LSTM. Here I will show with simple data.

import tensorflow as tf 
import numpy as np
layers = tf.keras.layers

timesteps = 32
channels = 16;
x = np.random.randn(100, timesteps, channels)

binary_y = np.random.randint(0, 2, size=(x.shape[0], 1))
reg_y = np.random.randn(x.shape[0], 1)

inputs = layers.Input(shape=(timesteps, channels))
hidden = layers.LSTM(32)(inputs)
out1 = layers.Dense(1, activation="sigmoid", name="binary_out")(hidden)
out2 = layers.Dense(1, activation=None, name="reg_out")(hidden)

model = tf.keras.Model(inputs=inputs, outputs=[out1, out2])

model.compile(loss={"binary_out":"binary_crossentropy", "reg_out":"mse"}, optimizer='adam', metrics={"binary_out":"accuracy"})

model.fit(x, [binary_y, reg_y], epochs=10)



Epoch 1/10
4/4 [==============================] - 0s 7ms/step - loss: 1.6842 - binary_out_loss: 0.6987 - reg_out_loss: 0.9855 - binary_out_accuracy: 0.5300
Epoch 2/10
4/4 [==============================] - 0s 6ms/step - loss: 1.6395 - binary_out_loss: 0.6937 - reg_out_loss: 0.9458 - binary_out_accuracy: 0.5400
Epoch 3/10
4/4 [==============================] - 0s 6ms/step - loss: 1.6124 - binary_out_loss: 0.6913 - reg_out_loss: 0.9211 - binary_out_accuracy: 0.5500
Epoch 4/10
4/4 [==============================] - 0s 7ms/step - loss: 1.5864 - binary_out_loss: 0.6886 - reg_out_loss: 0.8978 - binary_out_accuracy: 0.5600
Epoch 5/10
4/4 [==============================] - 0s 7ms/step - loss: 1.5660 - binary_out_loss: 0.6863 - reg_out_loss: 0.8797 - binary_out_accuracy: 0.5600
Epoch 6/10
4/4 [==============================] - 0s 7ms/step - loss: 1.5424 - binary_out_loss: 0.6832 - reg_out_loss: 0.8593 - binary_out_accuracy: 0.5500
Epoch 7/10
4/4 [==============================] - 0s 7ms/step - loss: 1.5206 - binary_out_loss: 0.6806 - reg_out_loss: 0.8400 - binary_out_accuracy: 0.5600
Epoch 8/10
4/4 [==============================] - 0s 6ms/step - loss: 1.5013 - binary_out_loss: 0.6785 - reg_out_loss: 0.8229 - binary_out_accuracy: 0.5600
Epoch 9/10
4/4 [==============================] - 0s 6ms/step - loss: 1.4816 - binary_out_loss: 0.6759 - reg_out_loss: 0.8057 - binary_out_accuracy: 0.5700
Epoch 10/10
4/4 [==============================] - 0s 6ms/step - loss: 1.4641 - binary_out_loss: 0.6737 - reg_out_loss: 0.7904 - binary_out_accuracy: 0.5800
user_58259530

So what you probably want and need to do is:

1, normalize/standardize data if not done so yet

2, you want to predict just one variable: most possible next price

3, you can use this next price to determine if the stock will rise or fall - simply compare it with previous price

I would recommend you to use recurrent network such as LSTM only if you have enough data (ie. daily/hourly... statistics) - if you have very limited dataset as in the example, basic regression algorithms should be just as fine.

As a side note, it is usually easier and more accurate to predict one label only by a single network/algorithm than all at once. Try to train N different models one for each label.

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