SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting

Excerpt

arXiv:2610.04109v1 Announce Type: cross Abstract: Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical numerical observations insufficient. While language models can retrieve external news, standard retrieval-augmented approaches struggle with high noise, missing signals, and an inability to reason causally about event impacts. We propose SEER (Self-Evolving Event Reasoning and Retrieval), a closed-l