DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting
arXiv:2604.01261v1 Announce Type: new 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 dynamical
arXiv:2604.01261v1 Announce Type: new 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.
Executive Summary
This article proposes DySCo, a novel framework for effective long-term time series forecasting. DySCo addresses the challenges of irrelevant noise and computational redundancy in traditional methods by introducing entropy-guided dynamic sampling, hierarchical frequency-enhanced decomposition, and cross-scale interaction mixing. Experimental results demonstrate DySCo's ability to enhance mainstream models' performance in capturing long-term correlations with reduced computational cost. While the framework shows promise, its applicability to diverse domains and scalability remain to be explored. As a plug-and-play module, DySCo has the potential to revolutionize time series forecasting, particularly in finance, meteorology, and energy sectors.
Key Points
- ▸ DySCo introduces Entropy-Guided Dynamic Sampling (EGDS) for autonomous identification of high-entropy segments and compression of redundant trends.
- ▸ Hierarchical Frequency-Enhanced Decomposition (HFED) separates high-frequency anomalies from low-frequency patterns.
- ▸ Cross-Scale Interaction Mixer (CSIM) dynamically fuses global contexts with local representations.
Merits
Strength in Addressing Computational Challenges
DySCo's EGDS and HFED mechanisms efficiently handle irrelevant noise and computational redundancy, enabling effective long-term time series forecasting.
Universal Plug-and-Play Module
DySCo's design allows for seamless integration with mainstream models, enhancing their performance without requiring significant modifications.
Demerits
Limited Scalability and Domain Applicability
The article does not provide a comprehensive evaluation of DySCo's performance across diverse domains and large-scale datasets, raising concerns about its practical applicability.
Dependence on Model Architectures
DySCo's effectiveness relies on the underlying model architecture, which may limit its adaptability to different forecasting tasks and models.
Expert Commentary
DySCo presents a significant advancement in time series forecasting by addressing computational challenges and enhancing model performance. However, its scalability and domain applicability require further investigation. The connection to transfer learning and explainability in time series forecasting offer promising avenues for future research. As a plug-and-play module, DySCo has the potential to revolutionize time series forecasting, but its adoption will depend on addressing the identified limitations and ensuring its explainability and interpretability.
Recommendations
- ✓ Investigate DySCo's performance on diverse datasets and across different forecasting tasks to ensure its scalability and domain applicability.
- ✓ Develop techniques to explain and interpret DySCo's outputs, enhancing its trustworthiness and adoption.
Sources
Original: arXiv - cs.LG
