EntroPE：基於熵的動態補丁編碼器用於時間序列預測

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

基於Transformer的模型在時間序列預測領域取得了顯著進展，其中基於片段（patch）的輸入策略提供了效率並改善了長時程建模。然而，現有方法依賴於時間無關的片段構建，即任意起始位置和固定長度會因跨越邊界分割自然過渡而破壞時間連貫性。這種簡單的分割方式常常擾亂短期依賴關係，削弱表示學習的效果。為此，我們提出了EntroPE（基於熵的動態片段編碼器），這是一種新穎的、具備時間感知的框架，它通過條件熵動態檢測過渡點並動態放置片段邊界。這樣既保留了時間結構，又保持了片段化的計算優勢。EntroPE包含兩個關鍵模塊：一是基於熵的動態片段器（EDP），它應用信息論標準來定位自然時間轉變並確定片段邊界；二是自適應片段編碼器（APE），它利用池化和交叉注意力來捕捉片段內依賴關係並生成固定大小的潛在表示。這些嵌入隨後由全局Transformer處理，以建模片段間的動態關係。在長期預測基準測試中的實驗表明，EntroPE在準確性和效率上均有提升，確立了基於熵的動態片段化作為時間序列建模的一種有前景的新範式。代碼可於以下網址獲取：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.