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