[2604.01261] DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting

Computer Science > Machine Learning arXiv:2604.01261 (cs) [Submitted on 1 Apr 2026] Title:DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting Authors:Xiang Ao, Yinyu Tan, Mengru Chen View a PDF of the paper titled DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting, by Xiang Ao and 2 other authors View PDF HTML (experimental) 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 ...