Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

The article introduces SERAF (Semantics-Enhanced Retrieval-Augmented Time Series Forecasting), a novel framework that enhances time series forecasting by incorporating dual retrieval of historical time series segments and their self-generated textual descriptions. This multimodal approach addresses limitations of traditional similarity-based retrieval methods, particularly in non-stationary contexts, and has been validated through experiments on seven real-world datasets, showing improved performance over existing state-of-the-art models. SERAF's integration of numerical and semantic data could significantly benefit practitioners by providing more robust forecasting capabilities in dynamic environments.