Characterizing the Limitations of Recurrent Neural Networks for Time Series Forecasting Using Distance Correlation

Recurrent neural networks (RNNs) have limitations in modeling time series with large lag structures, moving average processes, and heteroskedastic processes due to the gradual loss of information in their activation layers.