论文标题
具有多个季节性的预测时间序列的复发性神经网络：比较研究
Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study
论文作者
论文摘要
本文将复发性神经网络（RNN）与不同类型的门控单元进行比较，以预测具有多个季节性的预测时间序列。我们比较的细胞包括经典的长期记忆（LSTM），门控复发单元（GRU），经过修饰的LSTM随着扩张的速度以及我们最近提出的两个新细胞，这些细胞配备了扩张和注意机制。为了建模不同尺度的时间依赖性，我们的RNN架构具有多个扩张的复发层，并堆叠着分层扩张。提出的RNN为它们产生了点预测和预测间隔（PI）。一项关于35个欧洲国家短期电气负荷预测的实证研究证实，随着扩张和注意力的新封闭细胞表现最好。
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.
