def ts_train_test(all_data,time_steps,for_periods):
'''
input:
data: dataframe with dates and price data
output:
X_train, y_train: data from 2013/1/1-2018/12/31
X_test: data from 2019 -
sc: insantiated MinMaxScaler object fit to the training data
'''
# create training and test set
#all_data.iloc[:,[0, -1]].values
ts_train = all_data[:'2018'].iloc[:,[0, -1]].values
ts_test = all_data['2019':].iloc[:,[0, -1]].values
ts_train_len = len(ts_train)
ts_test_len = len(ts_test)
# create training data of s samples and t time steps
X_train = []
y_train = []
y_train_stacked = []
for i in range(time_steps,ts_train_len-1):
X_train.append(ts_train[i-time_steps:i,0:])
y_train.append(ts_train[i:i+for_periods,0:])
X_train, y_train = np.array(X_train), np.array(y_train)
# Reshaping X_train for efficient modelling
X_train = np.reshape(X_train, (X_train.shape[0],X_train.shape[1],2))
inputs = pd.concat((all_data[["Adj Close","Volume"]][:'2018'], all_data[["Adj Close","Volume"]]['2019':]),axis=0).values
inputs = inputs[len(inputs)-len(ts_test) - time_steps:]
inputs = inputs.reshape(-1,2)
#inputs
# Preparing X_test
X_test = []
for i in range(time_steps,ts_test_len+time_steps-for_periods):
X_test.append(inputs[i-time_steps:i,0:])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],2))
return X_train, y_train , X_test
X_train, y_train, X_test = ts_train_test(all_data,5,2)
X_train.shape[0],X_train.shape[1]
def simple_rnn_model(X_train, y_train, X_test):
'''
create single layer rnn model trained on X_train and y_train
and make predictions on the X_test data
'''
# create a model
from keras.models import Sequential
from keras.layers import Dense, SimpleRNN
my_rnn_model = Sequential()
my_rnn_model.add(SimpleRNN(64, return_sequences=True))
#my_rnn_model.add(SimpleRNN(32, return_sequences=True))
#my_rnn_model.add(SimpleRNN(32, return_sequences=True))
my_rnn_model.add(SimpleRNN(64))
my_rnn_model.add(Dense(2)) # The time step of the output
my_rnn_model.compile(optimizer='rmsprop', loss='mean_squared_error')
# fit the RNN model
my_rnn_model.fit(X_train, y_train, epochs=100, batch_size=150, verbose=0)
# Finalizing predictions
rnn_predictions = my_rnn_model.predict(X_test)
return my_rnn_model, rnn_predictions
my_rnn_model, rnn_predictions = simple_rnn_model(X_train, y_train, X_test)
rnn_predictions[1:10]
I read your article on medium
https://medium.com/swlh/a-technical-guide-on-rnn-lstm-gru-for-stock-price-prediction-bce2f7f30346
Modified part of the code to accept volume
but when I get to this part, I'm confused how to modify the hidden layers