一种基于距离相关性的方法：刻画循环神经网络在时间序列预测中的有效性
A Distance Correlation-Based Approach to Characterize the Effectiveness of Recurrent Neural Networks for Time Series Forecasting
AI总结：
本文提出基于距离相关性的方法，将时间序列特征与RNN组件关联，通过分析激活层信息流来解释性能差异，发现RNN能学习滞后结构但随层数加深而丢失信息，且无法充分建模移动平均和异方差过程，从而帮助从业者在无需训练的情况下评估RNN有效性。
摘要：
时间序列预测受到了广泛关注，其中循环神经网络（RNNs）因其处理序列数据的能力而成为广泛使用的模型之一。然而，以往关于RNN时间序列预测的研究显示出不一致的结果，并且很少对数据集之间的性能差异提供解释。在本文中，我们提供了一种方法，通过距离相关性的通用度量将时间序列特征与RNN组件联系起来。该度量使我们能够检查通过RNN激活层的信息流，从而能够解释和说明其性能。我们通过实验证明，RNN激活层能很好地学习时间序列的滞后结构。然而，它们在几个连续层的跨度内会逐渐丢失这些信息，从而恶化具有大滞后结构序列的预测质量。我们还表明，激活层无法充分建模移动平均和异方差时间序列过程。最后，我们生成了热图，用于可视化比较不同网络超参数选择下的激活层，以识别哪些超参数会影响预测性能。因此，我们的发现可以帮助从业者在不实际训练和评估网络的情况下，评估RNN对给定时间序列数据的有效性。
英文摘要：
Time series forecasting has received a lot of attention, with recurrent neural networks (RNNs) being one of the widely used models due to their ability to handle sequential data. Previous studies on RNN time series forecasting, however, show inconsistent outcomes and offer few explanations for performance variations among the datasets. In this paper, we provide an approach to link time series characteristics with RNN components via the versatile metric of distance correlation. This metric allows us to examine the information flow through the RNN activation layers to be able to interpret and explain their performance. We empirically show that the RNN activation layers learn the lag structures of time series well. However, they gradually lose this information over the span of a few consecutive layers, thereby worsening the forecast quality for series with large lag structures. We also show that the activation layers cannot adequately model moving average and heteroskedastic time series processes. Last, we generate heatmaps for visual comparisons of the activation layers for different choices of the network hyperparameters to identify which of them affect the forecast performance. Our findings can, therefore, aid practitioners in assessing the effectiveness of RNNs for given time series data without actually training and evaluating the networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
