N-ring: Redefining Multivariate Time-Series Forecasting with Length-N Directed Ring Pairs

Multivariate time-series forecasting is widely used in energy management, traffic monitoring, finance, and environmental sensing, yet many high-performing methods rely on deep architectures, complex correction modules, or temporal downsampling, increasing computational cost or risking information loss. We propose N-ring, a compact shallow model that decomposes long-horizon forecasting into structured local components without reducing the original sampling resolution. The model represents temporal dynamics with reusable directional rings and jointly learns their length, direction, phase, and local waveform, transforming monolithic value regression into coordinated dynamical-state composition. Across 32 input-output configurations and seven mainstream baselines, including PatchTST, DLinear, and MixLinear, the 3294–7244-parameter N-ring model achieves first place in 20/32 MAE and 6/32 MSE comparisons, and second place in 5/32 MAE and 6/32 MSE comparisons; relative to the two best lightweight baselines, MixLinear and SparseTSF, it reduces MAE by 3.02%–11.97% and MSE by 2.46%–9.34% across multiple datasets, with average reductions of 6.40% and 5.74%, respectively. These results establish structured dynamical-state decomposition and coordinated local specialization as an effective paradigm for compact and efficient long-horizon sequence modeling.