Correlated time series (CTS) forecasting is essential in many practical applications, such as traffic management and server load control. Various deep learning based solutions have been proposed to improve forecasting accuracy. However, while models have become increasingly computationally intensive, they strug- gle to improve accuracy. This study aims instead to enable more lightweight, accurate models suitable for resource-constrained de- vices. To achieve this goal, we characterize popular CTS forecasting models, yielding two observations for developing lightweight CTS forecasting. On this basis, we propose the LIGHTCTS framework that adopts plain stacking of temporal and spatial operators instead of alternate stacking which is much more computationally ex- pensive. Moreover, LIGHTCTS features light temporal and spatial operators, L-TCN and GL-Former, offering improved computa- tional efficiency without compromising their feature extraction capabilities. LIGHTCTS also encompasses a last-shot compression scheme to reduce redundant temporal features and speed up Fig. 1. DL-based CTS forecasting frameworks: (a) alternate stacking pattern, subsequent computations. Next, we equip LIGHTCTS with two and (b) plain stacking pattern. knowledge distillation modules, TAFD and CAAD, that result in LIGHTCTS retaining the original benefits of LIGHTCTS, while also being able to adapt to varying levels of ultra-constrained resources. Experimental studies offer detailed insight into these proposals correlations in correlated time series (CTS) is important in many and provide evidence that both LIGHTCTS and LIGHTCTS are applications, such as the forecasting of traffic situations [3], [4] capable of nearly state-of-the-art accuracy at substantially reduced and wind farm maintenance [5]. This study focuses on CTS computational costs. forecasting.