Closing Price prediction of Yahoo stocks from 2010 - 2016 using Gated Recurrant Units
Model is already trained and saved in 'stock_price_GRU.h5' file
To obtain the trained model just comment out the lines 47-55 and 60-62, then uncomment the lines 57-58 to load 'stock_price_GRU.h5' file
Highly Recommend using GPU version of Tensorflow for running the model
INPUT_DATA
date open low high close
2010-01-04 16.940001 16.879999 17.200001 17.100000
2010-01-05 17.219999 17.000000 17.230000 17.230000
2010-01-06 17.170000 17.070000 17.299999 17.170000
2010-01-07 16.809999 16.570000 16.900000 16.700001
2010-01-08 16.680000 16.620001 16.760000 16.700001
LABEL_DATA
date close
2010-01-04 17.230000
2010-01-05 17.170000
2010-01-06 16.700001
2010-01-07 16.700001
2010-01-08 16.740000
Layer (type) Output Shape Param #
gru_1 (GRU) (None, 1, 512) 794112
dropout_1 (Dropout) (None, 1, 512) 0
gru_2 (GRU) (None, 256) 590592
dropout_2 (Dropout) (None, 256) 0
dense_1 (Dense) (None, 1) 257
Total params: 1,384,961 Trainable params: 1,384,961 Non-trainable params: 0
Epoch 500/500
250/1061 [======>.......................] - ETA: 0s - loss: 7.2934e-04
750/1061 [====================>.........] - ETA: 0s - loss: 6.7267e-04
1061/1061 [==============================] - 0s 111us/step - loss: 6.4617e-04 - val_loss: 6.4601e-04
32/582 [>.............................] - ETA: 0s
352/582 [=================>............] - ETA: 0s
582/582 [==============================] - 0s 154us/step
Score: 0.000513115886573222
33% of Data used for Testing
Plot only shows the last points of test set and predicted values
