BucpTSF: Breaking the Uniform Computation Paradigm in Time-Series Forecasting

Real-world time series typically exhibit non-uniform information density across temporal positions, frequency structures, and forecasting horizons: critical dynamics are concentrated in local segments, different frequency components carry different structural information, and different horizons require different modeling complexity. However, most existing methods still allocate computation approximately uniformly across these dimensions, leading to redundant computation in low-information regions and insufficient modeling of key patterns. To address this, we propose BucpTSF, an information-density-driven structured framework for time series forecasting. Specifically, BucpTSF reorganizes raw sequences into hierarchically heterogeneous multi-level token representations through information-density-aware temporal representation reorganization, captures key cross-token dependencies with low overhead via frequency-selective structured token interaction, and further introduces horizon-conditioned structured extrapolation to adaptively balance dynamic modeling and structural extrapolation bias for different horizons, thereby improving the accuracy and stability of long-term forecasting. Extensive experiments on real-world datasets show that BucpTSF consistently outperforms strong baselines under various forecasting settings, validating the effectiveness of information-density-driven non-uniform computation allocation for time series forecasting.