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I use keras for time series prediction. My code can predict next 6 months by predict next one month and then get it to be input for predict next month again untill complete 6 months. That means predict one month 6 times. Can I predict next 6 month in one time.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from keras.layers import LSTM
from pandas.tseries.offsets import MonthEnd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout
import keras.backend as K
from keras.layers import Bidirectional
from keras.layers import Embedding
from keras.layers import GRU

df = pd.read_csv('D://data.csv',
 engine='python')

df['DATE_'] = pd.to_datetime(df['DATE_']) + MonthEnd(1)
df = df.set_index('DATE_')
df.head()

split_date = pd.Timestamp('03-01-2015')

train = df.loc[:split_date, ['data']]
test = df.loc[split_date:, ['data']]
sc = MinMaxScaler()

train_sc = sc.fit_transform(train)
test_sc = sc.transform(test)

X_train = train_sc[:-1]
y_train = train_sc[1:]

X_test = test_sc[:-1]
y_test = test_sc[1:]

K.clear_session()
model = Sequential()
model.add(Dense(12, input_dim=1, activation='relu'))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.summary()

model.fit(X_train, y_train, epochs=200, batch_size=2)

y_pred = model.predict(X_test)

real_pred = sc.inverse_transform(y_pred)
real_test = sc.inverse_transform(y_test)

print("Predict Value")
print(real_pred)

print("Test Value")
print(real_test)

Yes, by changing your output layer (the last layer) from Dense(1) to Dense(6). Of course you also have to change your y_train and y_test to have shape (1,6) instead of (1,1).

Best of luck.