What is the difference in ANN with lagged observation as predictors and LSTM? I am trying to understand how ANN is different from LSTM when we include lagged variables as predictors. Is the difference solely in forget gate for LSTM?

LSTMs inputs include "memory" in the form of the hidden and cell states, in addition to the raw features. ANNs with lagged observations only have lags of the raw features, but not memory.
