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.

本研究旨在创建轻量级的相关时间序列预测模型，LightCTS 框架采用时间和空间运算符的简单堆叠，以及最后压缩方案，以提高计算效率，同时在单步和多步预测基准数据集上也表现出接近最先进的准确性。