# 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()


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.

You have a problem in your code, you want to use "sigmoid" in the last layer. Fot the code you are showin you are using linear activation in the last layer.

You should not limit yourself to sigmoid as activation function on the last layer. Usually you're normalizing your dataset, but when you're testing/evaluating the model you're applying the inverse of the scaling transformation to the predictions, so you could easily use tanh which is defined on [-1, 1]