Zhihao Zhang

VRATF moves the comparison in retrieval-augmented forecasting from raw time series to denoised trend line charts: FFT low-pass filtering extracts the low-frequency trend, which is rendered as a 224×224 image and encoded by a frozen CLIP ViT-B/32 to build the retrieval database, with top-K neighbors selected by cosine similarity and aggregated by Gaussian-kernel weights. The retrieval forecast is fused at the prediction level with a DLinear base forecast through one learnable scalar. Across six multivariate benchmarks, VRATF reaches the lowest average MSE on four, with 0.340 on ETTh2 against 0.358 for the next-best RAFT, a 5.0% improvement.

VRATF moves the comparison in retrieval-augmented forecasting from raw time series to denoised trend line charts: FFT low-pass filtering extracts the low-frequency trend, which is rendered as a 224×224 image and encoded by a frozen CLIP ViT-B/32 to build the retrieval database, with top-K neighbors selected by cosine similarity and aggregated by Gaussian-kernel weights. The retrieval forecast is fused at the prediction level with a DLinear base forecast through one learnable scalar. Across six multivariate benchmarks, VRATF reaches the lowest average MSE on four, with 0.340 on ETTh2 against 0.358 for the next-best RAFT, a 5.0% improvement.