Skill4TSF: Contrastive Skill Distillation for Time Series Forecasting Agents

Time series forecasting exhibits substantial heterogeneity across datasets, forecasting horizons, and evolving temporal regimes, making it difficult for any single forecasting model or fixed pipeline to perform reliably in all settings. We propose Skill4TSF, a skill-based forecasting agent that learns reusable, condition-specific strategies for model selection and forecast refinement. Our key component, Contrastive Skill Distillation (CSD), derives skills from matched comparisons among heterogeneous forecasters. At the model level, CSD identifies which forecaster is preferable under particular time series characteristics using statistically supported performance differences. At the strategy level, it analyzes recurrent residual patterns on hard forecasting windows, proposes targeted interventions, including adaptive preprocessing, component-wise routing, and bias correction, and retains only those validated by paired statistical tests. Each skill explicitly represents its applicability conditions, recommended strategy, empirical evidence, and explanatory rationale. At inference time, Skill4TSF combines feature-based filtering with reasoning-based applicability assessment to retrieve and compose compatible skills for each input window. Experiments on eight real-world benchmarks show that Skill4TSF achieves the lowest average MSE on seven datasets and the lowest average MAE on six, with results averaged over four forecasting horizons. Ablations support the contributions of both distillation levels, while online adaptation and cross-backbone transfer experiments further examine the utility of learned skills. These results support evidence-grounded skill learning as a practical approach to adaptive time series forecasting.