Sensors in cyber-physical systems often capture interconnected processes and thus emit correlated time series (CTS), the forecasting of which enables important applications. The key to successful CTS forecasting is to uncover the temporal dynamics of time series and the spatial correlations among time series. Deep learning-based solutions exhibit impressive performance at discerning these aspects. In particular, automated CTS forecasting, where the design of an optimal deep learning architecture is automated, enables forecasting accuracy that surpasses what has been achieved by manual approaches. However, automated CTS solutions remain in their infancy and are only able to find optimal architectures for predefined hyperparameters and scale poorly to large-scale CTS. To overcome these limitations, we propose SEARCH, a joint, scalable framework, to automatically devise effective CTS forecasting models. Specifically, we encode each candidate architecture and accompanying hyperparameters into a joint graph representation. We introduce an efficient Architecture-Hyperparameter Comparator (AHC) to rank all architecture-hyperparameter pairs, and we then further evaluate the top-ranked pairs to select a final result. Extensive experiments on six benchmark datasets demonstrate that SEARCH not only eliminates manual efforts but also is capable of better performance than manually designed and existing automatically designed CTS models. In addition, it shows excellent scalability to large CTS.

翻译：信息物理系统中的传感器通常捕捉相互关联的过程，从而生成相关时间序列（CTS），对其预测可实现重要应用。成功预测CTS的关键在于揭示时间序列的时间动态特性以及不同时间序列间的空间相关性。基于深度学习的解决方案在识别这些方面展现出卓越性能。特别地，自动化CTS预测通过自动设计最优深度学习架构，实现了超越人工方法的预测精度。然而，当前自动化CTS解决方案仍处于初级阶段，仅能针对预定义超参数寻找最优架构，且难以扩展到大规模CTS场景。为解决这些局限，我们提出SEARCH——一个可扩展的联合框架，用于自动设计有效的CTS预测模型。具体而言，我们将每个候选架构及其对应超参数编码为联合图表示。我们引入高效的架构-超参数比较器（AHC）对所有架构-超参数对进行排序，随后进一步评估排名靠前的组合以选定最终结果。在六个基准数据集上的广泛实验表明，SEARCH不仅消除了人工设计负担，还能获得优于人工设计及现有自动设计CTS模型的性能。此外，该框架对大规模CTS展现出卓越的可扩展性。