EntroPE: Entropy-Guided Dynamic Patch Encoder for Time Series
  Forecasting
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.
68dc7f244159d1f2418f99e5
Sachith Abeywickrama
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2025-10-01T10:22:35.028Z
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Sachith Abeywickrama
user
sachithabey
68dc7f244159d1f2418f99e6
Emadeldeen Eldele
68dc7f244159d1f2418f99e7
Min Wu
68dc7f244159d1f2418f99e8
Xiaoli Li
68dc7f244159d1f2418f99e9
Chau Yuen
EntroPE: Entropy-Guided Dynamic Patch Encoder for Time Series
  Forecasting
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.
EntroPE, a temporally informed framework using entropy-guided dynamic patching, enhances time series forecasting by preserving temporal coherence and improving accuracy and efficiency.
Transformer-based models patch-based input strategies temporal coherence conditional entropy Entropy-based Dynamic Patcher Adaptive Patch Encoder pooling cross-attention global transformer long-term forecasting benchmarks