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

Abstract

Real-world time series are governed by both recurring structures, such as trends and seasonality, and infrequent yet critical variations, such as abrupt shifts and rare events. However, existing methods often lack an explicit mechanism to organize and utilize these heterogeneous patterns according to their distinct forecasting roles. Consequently, common and rare patterns can become entangled, preventing models from dynamically distinguishing and selectively leveraging them according to context. To address this issue, we propose Dual-Prototype Adaptive Disentanglement (DPAD), a model-agnostic framework that organizes temporal patterns by their forecasting roles. Specifically, we construct a Dynamic Dual-Prototype bank (DDP), comprising a common pattern bank initialized with structured temporal priors to represent prevalent dynamics, and a rare bank that adaptively memorizes infrequent deviations. Then a Dual-Path Context-aware routing (DPC) mechanism enhances outputs with selectively retrieved context-specific pattern representations from DDP. A Disentanglement-Guided Loss (DGLoss) is further introduced to ensure that each prototype bank specializes in its designated role while maintaining sufficient coverage. Extensive experiments across diverse real-world benchmarks demonstrate that DPAD consistently improves the forecasting performance of a range of time-series models.