Harnessing Oscillator Dynamics for High-Frequency Time Series Representation

High-quality representation of high-frequency components remains challenging in multivariate time series forecasting (TSF), due to their noisy and non-stationary patterns. Inspired by coupled oscillator dynamics, we introduce a Kuramoto oscillator-based high-frequency refiner (KOR), which maps high-frequency signals into a multivariate oscillator system and extracts structured latent responses from coupling-constrained phase evolution. Its restoring dynamics suppress rapid micro-fluctuations, while the observed phase-locking and slipping regimes admit a local Adler-type interpretation through effective frequency mismatch. To control the residual energy processed by KOR and complement spectral details not fully retained by its macroscopic readout, we further develop KOR-centered frequency modeling framework (KFM), consisting of soft-gated frequency decomposition and spectral fusion. Extensive experiments demonstrate that KFM improves the large majority of evaluated backbone–dataset settings and achieves competitive performance. Code will be open sourced upon acceptance.