动态压缩时间序列，让模型更高效捕捉长期依赖。
DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting
- 用熵引导自动筛选关键时序片段，压缩冗余信息。
- 分离高频异常与低频趋势，保留关键细节。
- 动态融合全局与局部特征，适合长序列预测场景。
时间序列预测在金融、气象、能源等领域至关重要。尽管延长回溯窗口理论上能提供更丰富的历史上下文，但实际中常引入无关噪声和计算冗余，导致模型难以有效捕捉复杂长期依赖。为此，我们提出动态语义压缩（DySCo）框架。不同于依赖固定启发式的方法，DySCo引入熵引导的动态采样（EGDS）机制，自主识别并保留高熵片段，同时压缩冗余趋势。此外，采用分层频率增强分解（HFED）策略，将高频异常与低频模式分离，确保稀疏采样时关键细节不丢失。最后设计跨尺度交互混合器（CSIM），动态融合全局上下文与局部表示，替代简单线性聚合。实验表明，DySCo作为通用即插即用模块，显著提升主流模型对长期相关性的建模能力，且计算成本更低。
原文摘要 · Abstract (English)
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.
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