univariate.py

from keras.models import Sequential
from keras.layers import Dense, Dropout,LSTM
from keras.optimizers import Adam
model = Sequential()
model.add(LSTM(units=100,input_shape=(x_train.shape[1],1),return_sequences=True))
model.add(LSTM(units=100))
model.add(Dropout(0.4))
model.add(Dense(1))
ADAM = Adam(0.0005, beta_1=0.9, beta_2=0.999, amsgrad=False)
model.compile(loss='mean_squared_error', optimizer=ADAM)
history = model.fit(x_train,y_train,epochs=50,batch_size=72,validation_data=(x_test,y_test),verbose=1,shuffle=False)
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='test')
plt.legend()
plt.show()