The key with autocorrelated models is to benchmark them against a naive alternative. I really appreciate Jakob's point that the LSTM might simply be modeling on

The key with autocorrelated models is to benchmark them against a naive alternative. I really appreciate Jakob's point that the LSTM might simply be modeling one step ahead using the current data point and Gaussian noise. Such a candid assessment is important in applied work. I suspect that, similar to other time series applications, you'll find some interesting signals from exogenous effects. I wonder how LSTMs can incorporate this exogenous information for time series analysis.