CrossRAG把RAG用到时间序列预测上，能检索历史相似形状，靠未来一致性挑参考，七个基准都超过了老方法。
CrossRAG框架将检索增强生成引入时间序列预测，使用Shape-Aware Memory（SAM）和RevIN归一化实现跨幅度的形状级检索。Future-Consistent Contrastive（FCC）学习用于区分历史相似但未来不一致的难负样本。Cross-Attention Temporal Fusion（CATF）在表示层融合检索到的历史-未来参考对。在七个公开基准测试上，CrossRAG的性能优于仅用参数的基线和现有检索增强预测方法。
TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion
Large-scale multivariate time series from heterogeneous IoT sensors demand accurate long-term forecasting for resource scheduling and predictive maintenance. While recent time series foundation models exhibit strong generalization, they rely on static parametric knowledge and lack dynamic access to external historical patterns during inference. Retrieval-Augmented Generation (RAG) offers a potential remedy, yet its application to time series forecasting is challenged by magnitude variations across heterogeneous sources and the mismatch between historical similarity and future consistency. We propose CrossRAG, a retrieval-augmented forecasting framework that integrates Shape-Aware Memory (SAM) with RevIN normalization for magnitude-robust shape-level retrieval, Future-Consistent Contrastive (FCC) learning to distinguish informative references from hard negatives with similar history but divergent futures, and Cross-Attention Temporal Fusion (CATF) to fuse retrieved historical--future reference pairs into the backbone's representations at the representation level. Experiments on seven public benchmarks show that CrossRAG consistently outperforms both parametric-only baselines and existing retrieval-augmented forecasting methods.