TS Model

prediction_days=1
model = Sequential()
model.add(LSTM(100, input_shape=(train_X.shape[1],
                                train_X.shape[2]),
                       return_sequences=True))

model.add(LSTM(100, input_shape=(train_X.shape[1],
                                     train_X.shape[2])))
model.add(Dense(75,
                    activation= "relu",
                    input_dim = lookback*features))
model.add(Dense(100,
                    activation= "relu",
                    input_dim = lookback*features))

model.add(Dense(prediction_days))
model.compile(loss='mean_squared_error', optimizer='adam')

history = model.fit(train_X,
                    train_Y,
                    epochs=100,
                    batch_size=21,
                    validation_data=(validation_X, validation_Y),
                    verbose=0,
                    shuffle=False,
                    callbacks=[TQDMNotebookCallback()])