The rapid development of time series forecasting research has brought many deep learning-based modules in this field. However, despite the increasing amount of new forecasting architectures, it is still unclear if we have leveraged the full potential of these existing modules within a properly designed architecture. In this work, we propose a novel hierarchical neural architecture search approach for time series forecasting tasks. With the design of a hierarchical search space, we incorporate many architecture types designed for forecasting tasks and allow for the efficient combination of different forecasting architecture modules. Results on long-term-time-series-forecasting tasks show that our approach can search for lightweight high-performing forecasting architectures across different forecasting tasks.

翻译：时间序列预测研究的快速发展催生了众多基于深度学习的预测模块。然而，尽管新型预测架构不断涌现，我们仍不清楚是否已在合理设计的架构中充分利用了这些现有模块的全部潜力。本研究提出了一种新颖的分层神经架构搜索方法，专门用于时间序列预测任务。通过设计分层搜索空间，我们整合了多种为预测任务设计的架构类型，并实现了不同预测架构模块的高效组合。在长期时间序列预测任务上的实验结果表明，我们的方法能够在不同预测任务中搜索到轻量级且高性能的预测架构。