Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting

TL;DR

This work tackles the challenge of static representations in time series forecasting under non-stationarity. It introduces DPAD, a model-agnostic auxiliary framework built around a Dynamic Dual-Prototype Bank (DDP) consisting of a Common Pattern Bank (B_c) and a Rare Pattern Bank (B_r), plus a Dual-Path Context-Aware Routing (DPC) mechanism and a Disentanglement-Guided Loss (DGLoss) to enforce specialization and diversity. The approach is validated across seven real-world datasets and multiple backbones, showing consistent improvements with minimal overhead and promising zero-shot transfer to unseen data. By enabling context-aware pattern disentanglement, DPAD enhances robustness to distribution shifts and rare events, with practical implications for deploying reliable forecasting across domains.

Abstract

Time series forecasting has witnessed significant progress with deep learning. While prevailing approaches enhance forecasting performance by modifying architectures or introducing novel enhancement strategies, they often fail to dynamically disentangle and leverage the complex, intertwined temporal patterns inherent in time series, thus resulting in the learning of static, averaged representations that lack context-aware capabilities. To address this, we propose the Dual-Prototype Adaptive Disentanglement framework (DPAD), a model-agnostic auxiliary method that equips forecasting models with the ability of pattern disentanglement and context-aware adaptation. Specifically, we construct a Dynamic Dual-Prototype bank (DDP), comprising a common pattern bank with strong temporal priors to capture prevailing trend or seasonal patterns, and a rare pattern bank dynamically memorizing critical yet infrequent events, and then an Dual-Path Context-aware routing (DPC) mechanism is proposed to enhance outputs with selectively retrieved context-specific pattern representations from the DDP. Additionally, we introduce a Disentanglement-Guided Loss (DGLoss) to ensure that each prototype bank specializes in its designated role while maintaining comprehensive coverage. Comprehensive experiments demonstrate that DPAD consistently improves forecasting performance and reliability of state-of-the-art models across diverse real-world benchmarks.

Figures (8)

• Figure 1: Limitation of static and averaged representations in time series forecasting. We visualize three characteristic failure scenarios where some methods fall short: (a) abrupt distribution shifts, (b) intertwined complex patterns, and (c) critical rare events. These limitations motivate our proposed context-aware enhancement framework.
• Figure 2: The overall architecture of the DPAD framework. The framework constructs a learnable dual-prototype bank, then performs adaptive dual-path routing on the input data to retrieve relevant prototypes, and finally generates a context-aware enhancement for prediction. The entire framework is optimized under a disentanglement-guided loss.
• Figure 3: Visualization of learned prototypes in DDP on the Electricity dataset.
• Figure 4: Forecasting results (MSE and MAE) with varying look-back length $\{48, 96, 192, 336, 720\}$ on Electricity dataset. Prediction length is fixed to 96.
• Figure 5: Hyperparameter sensitivity analysis with varying sizes of prototype banks on Weather dataset. Here we use iTransformer as backbone. Left is of common pattern set, right is of rare patterns set. The look-back length is fixed to 96.