Using sigmoid in LSTM network for multi-step forecasting I'm trying to develop a multistep forecasting model using LSTM  Network. The model takes three times steps as input and predicting two time_steps. both input and output columns are normalised using minmax_scalar within the range of 0 and 1.

Please see the below model architecture

Model Architecture 

model = Sequential()
model.add(LSTM(80,input_shape=(3,1),activation='sigmoid',return_sequences=True))
model.add(LSTM(20,activation='sigmoid',return_sequences=False))
model.add(Dense(2))


In this case, using sigmoid as an activation function is it correct?
Yes, due the input, output being constrained between zero and one that would be the only viable activation function.