TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster

Understand

Large Language Models (LLMs) and Foundation Models (FMs) have recently become prevalent for time series forecasting tasks.

• While fine-tuning LLMs enables domain adaptation, they often struggle to generalize across diverse and unseen datasets.
• Moreover, existing Time Series Foundation Models (TSFMs) still face challenges in handling non-stationary dynamics and distribution shifts, largely due to the lack of effective mechanisms for adaptation.
• To this end, we present TS-RAG, a retrieval-augmented generation framework for time series forecasting that enhances the generalization and interpretability of TSFMs.