Rethinking RAG for Time Series: Does Similarity Really Lead to Predictive Utility?

Historical patterns beyond the input context can provide useful information for time-series forecasting. Retrieval-augmented forecasting seeks to exploit such information by retrieving historical segment candidates whose contexts are similar to the query context. This raises a fundamental question: does similarity reliably indicate the predictive utility of a historical candidate? We examine it from three perspectives: (1) how prevalent useful historical candidates are, (2) whether similarity-based retrieval can identify them, and (3) whether their predictive utility remains consistent across augmentation interfaces. Across 24 experimental settings, our empirical results reveal several important findings. Useful candidates are common: 38.32–56.23% of candidates improve the forecast, with the best candidates yielding gains of 24.56–71.01%. Surprisingly, similarity-based retrieval underperforms random sampling in 83.33% of retrieval comparisons. Predictive utility is also strongly interface-dependent: in 55.56% of cross-interface transfers, historical candidates selected as optimal under the source interface yield negative gains under the target interface. Our findings suggest that similarity alone is insufficient for retrieval, and future retrieval methods should better account for which historical candidates are actually useful under the chosen augmentation interface.