Beyond Numerical Similarity: Resolution-Variant Visual Retriever for Time Series Forecasting

Retrieval-augmented time series forecasting relies on identifying historical segments that provide relevant predictive context. However, a single numerical similarity metric is constrained in capturing global and local temporal patterns. Viewing time series as visual trajectories makes trends, fluctuations, and turning points explicit, but exploiting this information requires both a comprehensive representation and a quantifiable similarity measurement for retrieval. In this work, we propose $\text{ReViR}$, a visual retrieval method that represents temporal patterns through triangle regions and measures their geometric agreement. Specifically, Visual Sampling selects visual anchors, and the triangle regions induced by these anchors form a region map for each time series. For retrieval similarity, we introduce Approximate Triangle-IoU, which measures region overlap on a shared time-value grid, providing an interpretable visual similarity without a pretrained vision encoder. Historical time series selected by the visual similarity are aggregated and fused with a lightweight forecaster. To control the overhead of visual retrieval, configurable sampling density and resolution balance representation content against cost, while packed binary masks enable efficient overlap computation. Experiments on seven real-world benchmarks demonstrate that ReViR achieves state-of-the-art forecasting performance, while substantially reducing retrieval-library storage in the evaluated settings.