Dual-Context Analog Retrieval for Time-Series Forecasting

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 determine which historical state is most relevant to each future segment or consider what followed that state. Analog forecasting can provide such evidence by retrieving a past state similar to the present and examining its observed continuation. However, relying on a single nearest match can be unreliable, and overlapping patches may yield highly similar or near-duplicate candidates. To address these limitations, we propose DuoTS, a Dual-Context Time Series forecasting model, which leverages retrieved evidence without depending on it exclusively. DuoTS first generates a base forecast using a fully parallel patch encoder and a linear prediction head, and then progressively refines this forecast one future patch at a time. Each refinement considers two complementary views. The first view, the current context, attends to recent tokens and captures the latest dynamics of the series. The second view, the detail context, provides distinct retrieved analogs together with the trajectories that followed them. Since the refinement is performed separately for each future patch, the model weighs the two views differently across the forecast horizon and associates each segment of the prediction with the evidence most appropriate to its temporal distance from the present. Extensive experiments on multiple real-world time-series datasets show that DuoTS achieves state-of-the-art performance, while component-wise ablation studies verify the contribution of each context. Moreover, the refinement mechanism is independent of the remaining DuoTS architecture. It requires only the encoded look-back window and the position of the future patch, allowing it to be applied as a model-agnostic module to improve existing forecasting models.