Bridging Horizon-Specific Representations for Time Series Forecasting

Existing time series forecasting methods typically train separate models for each forecasting horizon, as different horizons demand distinct representations. However, such a horizon-specific method limits its flexibility in real-world scenarios with diverse forecasting demands. In this paper, we provide a theoretical analysis, which reveals that optimal representation can vary with the horizon: short-term forecasting relies on local dynamic patterns, while long-term forecasting depends more on global trends. We thereby propose HorizonBridge, a novel framework that leverages a representation translator to bridge horizon-specific representations for cross-horizon forecasting. Specifically, HorizonBridge shares a base representation encoder across different horizons to preserve common structure. Then, we devise a horizon-conditioned representation translator to generate adaptive residual adjustments for the base representation, thereby adapting it to each target horizon. To further improve cross-horizon representation compatibility, we incorporate an adaptive patch embedding mechanism that partitions time series according to local information density and leverages temporal periodicity to construct cyclical embeddings. Extensive experiments show that HorizonBridge achieves competitive forecasting performance across multiple benchmark datasets, while offering improved efficiency and flexibility. The source code and models will be released.