Dual-Context Analog Retrieval for Time Series Forecasting

2026-10-02

AI Summary

This study addresses the limitations of single-step mapping, which neglects historical dependencies, and the unreliability of traditional retrieval-based matching in long-term time series forecasting. To this end, we propose DuoTS, a model-agnostic framework that employs parallel patch encoding to generate base predictions. It innovatively introduces a dual-context mechanism that integrates local dynamics with global analogical evidence, alongside a segment-wise progressive refinement strategy to balance historical information across varying temporal distances, thereby achieving patch-level optimization. Extensive experiments demonstrate that DuoTS attains state-of-the-art performance on multiple real-world datasets. Furthermore, the proposed refinement mechanism can be seamlessly integrated into existing forecasting models, and ablation studies comprehensively validate the effectiveness of each individual component.

Abstract

Most long-term time-series forecasting models map the look-back window directly to the full horizon in a single pass. While efficient, this design does not explicitly identify which historical states are most relevant to different future segments or exploit what followed those states. Analog forecasting addresses this by retrieving past states similar to the present and using their observed continuations, but single nearest matches can be unreliable and overlapping patches may produce redundant candidates. We propose DuoTS, a Dual-Context Time Series forecasting model that uses retrieved evidence without relying on it exclusively. DuoTS first produces a base forecast with a parallel patch encoder and linear prediction head, then progressively refines it one future patch at a time. Each refinement combines two views: a current context that attends to recent tokens and captures the latest dynamics, and a detail context that provides distinct retrieved analogs together with their subsequent trajectories. Patch-wise refinement allows the model to balance these views across the forecast horizon and associate each future segment with evidence appropriate to its temporal distance from the present. Experiments on multiple real-world datasets show that DuoTS achieves state-of-the-art performance, while ablations confirm the contribution of each context. The refinement mechanism is also model-agnostic, requiring only an encoded look-back window and the future-patch position, and can therefore be integrated into existing forecasting models.

Problem

• Time Series Forecasting
• Analog Retrieval
• Long-term Prediction
• Patch-wise Refinement

Innovation

• Dual-Context
• Analog Retrieval
• Patch-wise Refinement
• Time Series Forecasting
• Model-Agnostic