DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting

Abstract

Time series forecasting (TSF) is critical across domains such as finance, meteorology, and energy. While extending the lookback window theoretically provides richer historical context, in practice, it often introduces irrelevant noise and computational redundancy, preventing models from effectively capturing complex long-term dependencies. To address these challenges, we propose a Dynamic Semantic Compression (DySCo) framework. Unlike traditional methods that rely on fixed heuristics, DySCo introduces an Entropy-Guided Dynamic Sampling (EGDS) mechanism to autonomously identify and retain high-entropy segments while compressing redundant trends. Furthermore, we incorporate a Hierarchical Frequency-Enhanced Decomposition (HFED) strategy to separate high-frequency anomalies from low-frequency patterns, ensuring that critical details are preserved during sparse sampling. Finally, a Cross-Scale Interaction Mixer(CSIM) is designed to dynamically fuse global contexts with local representations, replacing simple linear aggregation. Experimental results demonstrate that DySCo serves as a universal plug-and-play module, significantly enhancing the ability of mainstream models to capture long-term correlations with reduced computational cost.

Figures (7)

• Figure 1: Predictive performance (measured by MSE) under different lookback windows. The "Hoped" denotes the results conforming to common sense.
• Figure 2: Competition of different lookback windows. The short lookback window is derived from the long lookback window spanning steps 600 to 700. Note how the long window captures the trend but obscures the local periodicity visible in the short window.
• Figure 3: Framework of DySCo. The left panel illustrates the overall basic framework of DySCo, which comprises the HFED module and prediction-related components; the right panel details the workflow of the EGDS module.
• Figure 4: Efficiency comparison of basic models versus DySCo-integrated models across three representative datasets. The evaluation includes model parameters (left), GPU memory consumption (center), and training time per epoch (right). DySCo consistently demonstrates significant reductions across all computational metrics despite utilizing a multi-branch architecture.
• Figure 5: Visualization on ETTh1: DySCo effectively captures the long-term trends underlying the noisy historical data, whereas the baseline model struggles with information redundancy.