Correlated time series (CTS) forecasting plays an essential role in many practical applications, such as traffic management and server load control. Many deep learning models have been proposed to improve the accuracy of CTS forecasting. However, while models have become increasingly complex and computationally intensive, they struggle to improve accuracy. Pursuing a different direction, this study aims instead to enable much more efficient, lightweight models that preserve accuracy while being able to be deployed on resource-constrained devices. To achieve this goal, we characterize popular CTS forecasting models and yield two observations that indicate directions for lightweight CTS forecasting. On this basis, we propose the LightCTS framework that adopts plain stacking of temporal and spatial operators instead of alternate stacking that is much more computationally expensive. Moreover, LightCTS features light temporal and spatial operator modules, called L-TCN and GL-Former, that offer improved computational efficiency without compromising their feature extraction capabilities. LightCTS also encompasses a last-shot compression scheme to reduce redundant temporal features and speed up subsequent computations. Experiments with single-step and multi-step forecasting benchmark datasets show that LightCTS is capable of nearly state-of-the-art accuracy at much reduced computational and storage overheads.

翻译：相关时间序列（CTS）预测在交通管理和服务器负载控制等诸多实际应用中发挥着关键作用。为提升CTS预测精度，研究者已提出多种深度学习模型。然而，尽管模型日趋复杂且计算密集度不断提高，其精度提升仍面临瓶颈。本研究另辟蹊径，旨在开发更高效、更轻量化的模型，在保持预测精度的同时能够部署于资源受限设备。为此，我们通过对主流CTS预测模型进行特征分析，获得两项指导轻量化CTS预测的观测结论。基于此，我们提出LightCTS框架，该框架采用时序算子与空间算子的简单堆叠结构，替代计算开销更高的交替堆叠方案。此外，LightCTS设计了轻量化时序与空间算子模块L-TCN和GL-Former，在保持特征提取能力的前提下显著提升计算效率。同时，LightCTS引入末步压缩机制以消除冗余时序特征并加速后续计算。在单步与多步预测基准数据集上的实验表明，LightCTS能以显著降低的计算与存储开销取得接近最优的预测精度。