TimeRift: Learning Background-Relative Dynamics for Multivariate Time Series Forecasting

Similar local dynamics in multivariate time series can evolve into markedly different future trajectories under distinct recurrent temporal backgrounds. Although existing methods primarily incorporate temporal context or disentangle periodic components, they fail to explicitly model how recurrent temporal backgrounds shape the evolution of local dynamics. To address this critical issue, we propose TimeRift, a forecasting framework that explicitly models background-conditioned dynamics. Specifically, TimeRift firstly learns recurrent temporal background prototypes from recurrent temporal indices and represents each input as a deviation from its corresponding background, which serves as both a reference for local dynamics and a conditioning signal for subsequent evolution. Further, TimeRift calibrates background-conditioned deviations via context-dependent scaling and shifting, and then evolves them using adaptive spectral weights to fuse global patterns with background-specific adjustments. Finally, the evolved dynamics are recombined with the temporal background to obtain the forecasting. Comprehensive experiments on seven real-world benchmarks demonstrate that TimeRift achieves competitive forecasting accuracy with low computational overhead. Moreover, ablation and controlled intervention studies further confirm the contribution of background conditioning for accurate forecasting. Our code is available at https://anonymous.4open.science/r/TimeRift.