优化时间序列预测架构：一种层次化神经架构搜索方法
Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach
AI总结：
本文提出一种层次化神经架构搜索方法，通过设计层次化搜索空间高效组合多种预测模块，在长期时间序列预测任务上搜索出轻量级高性能架构。
摘要：
时间序列预测研究的快速发展为该领域带来了许多基于深度学习的模块。然而，尽管新的预测架构数量不断增加，我们仍不清楚是否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。在这项工作中，我们提出了一种新颖的层次化神经架构搜索方法，用于时间序列预测任务。通过设计层次化搜索空间，我们纳入了多种专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结果表明，我们的方法能够跨不同预测任务搜索出轻量级且高性能的预测架构。
英文摘要：
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.
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。
