Stationarity-Aware Retrieval-Augmented Time Series Forecasting

In three linesSARAF, a retrieval-augmented time series forecasting framework, adaptively balances relevance and diversity based on dataset stationarity. It selects heterogeneous historical segments and aggregates their futures with stationarity-aware fusion. Experiments on 8 real-world datasets show improved accuracy and robustness over baselines.

Summary generated by Claude — human-verified