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众多实际应用中发挥着重要作用。为提升CTS预测精度，研究者已提出诸多深度学习模型。然而，随着模型日益复杂且计算开销剧增，其精度提升却愈发困难。本研究另辟蹊径，旨在构建更高效、轻量化的模型，使其在保持精度的同时能部署于资源受限设备。为此，我们通过分析主流CTS预测模型特征，得出两项指导轻量化CTS设计的关键观察。在此基础上，提出LightCTS框架——采用时间算子与空间算子纯堆叠结构，替代计算成本显著更高的交替堆叠方案。该框架的核心轻量模块L-TCN与GL-Former，在保持特征提取能力的同时显著提升计算效率。LightCTS还集成末次压缩机制，用于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能以极低的计算与存储开销实现近乎最优的预测精度。