Decomposed Local-to-Global Retrieval for Time Series Forecasting

Time series forecasting fundamentally requires capturing both short-term dynamics and long-term structural patterns. While parametric models often struggle to internalize these diverse dependencies from limited data, retrieval-based approaches offer a promising alternative by explicitly reusing informative historical patterns. However, existing methods typically perform retrieval in a monolithic feature space, overlooking that varying temporal structures (\eg, trend vs. seasonality) require distinct matching mechanisms. In this paper, we propose DLoG (Decomposed Local-to-Global retrieval), a framework that augments forecasting by leveraging decomposed historical patterns as inductive biases. DLoG specifically addresses the multi-scale nature of time series through two key designs: (1) Component-wise Retrieval, which decomposes series into complementary components to enable targeted pattern alignment; and (2) Dual-stage Retrieval, which first retrieves from a local window to preserve short-term consistency, followed by a global search to incorporate long-range structural regularities. By integrating these component-specific future references, DLoG reduces the burden on purely parametric forecasting by explicitly reusing informative historical patterns. Extensive experiments on nine benchmark datasets show that DLoG achieves best or second-best average MSE and MAE across all evaluated datasets, demonstrating strong and consistent performance against competitive forecasting baselines.