  This paper compares recurrent neural networks (RNNs) with different types of
gated cells for forecasting time series with multiple seasonality. The cells we
compare include classical long short term memory (LSTM), gated recurrent unit
(GRU), modified LSTM with dilation, and two new cells we proposed recently,
which are equipped with dilation and attention mechanisms. To model the
temporal dependencies of different scales, our RNN architecture has multiple
dilated recurrent layers stacked with hierarchical dilations. The proposed RNN
produces both point forecasts and predictive intervals (PIs) for them. An
empirical study concerning short-term electrical load forecasting for 35
European countries confirmed that the new gated cells with dilation and
attention performed best.
