Beyond Model Libraries: Learning to Compose Forecasting Architectures for Heterogeneous Time Series

Time-series forecasting models often exhibit heterogeneous performance across series with diverse temporal patterns, making it challenging to identify a universally optimal architecture. Existing approaches either select from predefined model libraries or rely on large-scale pretrained models, but they are limited by restricted structural coverage, high computational cost, or insufficient adaptation to individual series. In this paper, we propose **RankTS**, a component-level model selection framework that learns architectural preferences from historical forecasting tasks and transfers them to unseen series. RankTS constructs a diverse compositional search space from forecasting operators, explores effective architectures through task-local evolution, and learns a series-conditioned ranker from sparse architecture evaluations. By jointly modeling time-series characteristics and architecture configurations, RankTS efficiently generates and selects suitable forecasting architectures without exhaustive search on each target series. Extensive experiments on five time-series datasets and four forecasting horizons demonstrate that RankTS consistently outperforms existing model selection strategies and achieves competitive performance against strong forecasting model families with limited target-side training.