TFGformer: Multivariate Time Series Forecasting via Time-Frequency Graph Learning and Covariate Fusion

Forecasting large-scale IoT sensor data over long time horizons is critical for maintenance and scheduling, but current time-series foundation models rely on static learned patterns without accessing relevant historical examples at inference time. CrossRAG solves this with retrieval-augmented forecasting: shape-aware memory retrieval robust to magnitude differences, contrastive learning that filters out misleadingly similar-but-divergent historical references, and cross-attention fusion of retrieved data into predictions. Tested on seven benchmarks, it outperforms both standard and existing retrieval-based forecasting methods. This is useful for industrial IoT monitoring, energy grid management, and any long-horizon forecasting task involving heterogeneous sensor networks. Authors: Yu Sun, Yuan Chang, Xiaohou Shi, Yan Sun Paper: https://arxiv.org/abs/2607.29459v1