标题： 基于分层注意力的循环高速公路网络的时间序列预测 显示英文标题

标题： Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction

摘要： 时间序列预测已在多个领域中得到研究。 然而，给定历史观测和过去的外部数据，预测未来序列仍然具有挑战性。 现有方法要么未能考虑外部变量不同组件之间的相互作用，这可能会影响预测准确性，要么无法对齐外部数据与目标数据之间的相关性。 此外，外部数据的固有时间动态也与目标序列预测有关，因此也应予以考虑。 为了解决这些问题，我们提出了一种端到端的深度学习模型，即基于层次注意力的循环高速公路网络（HRHN），该模型将外部变量的时空特征提取和目标变量的时间动态建模整合到一个框架中。 此外，通过引入层次注意力机制，HRHN可以在不同语义层次上自适应地选择相关的外部特征。 我们在多个数据集上与各种方法进行了全面的实证评估，并表明HRHN在时间序列预测中优于最先进的方法，尤其是在捕捉时间序列的突然变化和突然波动方面。 显示英文摘要

摘要： Time series prediction has been studied in a variety of domains. However, it is still challenging to predict future series given historical observations and past exogenous data. Existing methods either fail to consider the interactions among different components of exogenous variables which may affect the prediction accuracy, or cannot model the correlations between exogenous data and target data. Besides, the inherent temporal dynamics of exogenous data are also related to the target series prediction, and thus should be considered as well. To address these issues, we propose an end-to-end deep learning model, i.e., Hierarchical attention-based Recurrent Highway Network (HRHN), which incorporates spatio-temporal feature extraction of exogenous variables and temporal dynamics modeling of target variables into a single framework. Moreover, by introducing the hierarchical attention mechanism, HRHN can adaptively select the relevant exogenous features in different semantic levels. We carry out comprehensive empirical evaluations with various methods over several datasets, and show that HRHN outperforms the state of the arts in time series prediction, especially in capturing sudden changes and sudden oscillations of time series.