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DuoTS addresses long-term time-series forecasting by refining a base prediction patch-by-patch, integrating a current context for recent dynamics and a detail context of retrieved analogs with their observed continuations. This dual-context approach allows for dynamic weighting of evidence across the forecast horizon, associating each future segment with temporally appropriate historical data. Experiments demonstrate state-of-the-art performance, validating the contribution of both contextual views and the patch-wise refinement mechanism.
Time series models can achieve state-of-the-art long-term forecasting by dynamically blending recent dynamics with retrieved historical trajectories, rather than relying solely on a single look-back window.
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.