AI

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

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Briefings

SEER is a closed-loop framework designed to improve time series forecasting by dynamically optimizing event conditioning. It addresses limitations in standard retrieval-augmented approaches by translating prediction errors into two decoupled feedback mechanisms: a reflective retrieval memory that refines search queries and filters noise, and a persistent causal knowledge base that distills transferable domain dynamics. The system enforces strict chronological boundaries to prevent look-ahead bias and data leakage. Across six volatile time-series benchmarks, SEER consistently outperforms state-of-the-art time series foundation models and language model baselines.

Key points

• SEER uses a reflective retrieval memory to refine search queries and filter noise based on prediction errors.
• SEER maintains a persistent causal knowledge base to distill transferable domain dynamics from time series data.

Context

Standard retrieval-augmented approaches struggle with high noise, missing signals, and an inability to reason causally about event impacts. SEER addresses these issues by translating prediction errors into decoupled feedback mechanisms for better forecasting accuracy.

Limitations

Evidence covers six volatile time-series benchmarks. Results are from a single snapshot on October 1, 2026, and do not include long-term performance data or comparisons with other causal modeling techniques.

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