way to add target delay on TimeseriesGenerator from keras.preprocessing.sequence
You can use 


  pandas shift


By default the TimeseriesGenerator performs a lag of 1 timestep. Then, a workaraound is to create a pandas Series, and use the shift function. 
Let train be your sequence, and let forecast be an int representing the "size" of your lag. 

Eg:

label_train = pd.Series(train)    
train_generator = TimeseriesGenerator(train, label_train.shift(-forecast+1).values)


Obs: Be careful to pad your sequence after performing the lag.