EntroPE：面向时间序列预测的熵引导动态补丁编码器

EntroPE: Entropy-Guided Dynamic Patch Encoder for Time Series Forecasting

September 30, 2025

作者: Sachith Abeywickrama, Emadeldeen Eldele, Min Wu, Xiaoli Li, Chau Yuen

cs.AI

摘要

基于Transformer的模型在时间序列预测领域取得了显著进展，其中基于分块的输入策略提供了高效性并改进了长时程建模。然而，现有方法依赖于时间无关的分块构建，即任意起始位置和固定长度通过跨越边界分割自然过渡，破坏了时间连贯性。这种简单的分段方式常常打断短期依赖关系，削弱了表示学习的效果。为此，我们提出了EntroPE（熵引导的动态分块编码器），这是一种新颖的、时间感知的框架，它通过条件熵动态检测过渡点，并动态放置分块边界。这一方法在保留分块计算优势的同时，维护了时间结构。EntroPE包含两个关键模块：一是基于熵的动态分块器（EDP），它应用信息论准则定位自然时间变化点并确定分块边界；二是自适应分块编码器（APE），它利用池化和交叉注意力机制捕捉分块内依赖关系，生成固定大小的潜在表示。这些嵌入随后由全局Transformer处理，以建模分块间的动态关系。在长期预测基准测试中的实验表明，EntroPE在提升准确性和效率方面均表现出色，确立了熵引导的动态分块作为时间序列建模的一个有前景的新范式。代码已发布于：https://github.com/Sachithx/EntroPE。

English

Transformer-based models have significantly advanced time series forecasting, with patch-based input strategies offering efficiency and improved long-horizon modeling. Yet, existing approaches rely on temporally-agnostic patch construction, where arbitrary starting positions and fixed lengths fracture temporal coherence by splitting natural transitions across boundaries. This naive segmentation often disrupts short-term dependencies and weakens representation learning. In response, we propose EntroPE (Entropy-Guided Dynamic Patch Encoder), a novel, temporally informed framework that dynamically detects transition points via conditional entropy and dynamically places patch boundaries. This preserves temporal structure while retaining the computational benefits of patching. EntroPE consists of two key modules, namely an Entropy-based Dynamic Patcher (EDP) that applies information-theoretic criteria to locate natural temporal shifts and determine patch boundaries, and an Adaptive Patch Encoder (APE) that employs pooling and cross-attention to capture intra-patch dependencies and produce fixed-size latent representations. These embeddings are then processed by a global transformer to model inter-patch dynamics. Experiments across long-term forecasting benchmarks demonstrate that EntroPE improves both accuracy and efficiency, establishing entropy-guided dynamic patching as a promising new paradigm for time series modeling. Code is available at: https://github.com/Sachithx/EntroPE.