SEER：面向时间序列预测的自演化事件推理与检索
SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting
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中文总结 AI 辅助
针对传统预测难以应对事件驱动的时间序列，SEER提出闭环框架，通过反思性检索记忆和因果知识库动态优化事件条件，并在六个基准上超越现有模型。
中文摘要 AI 辅助
真实世界的时间序列经常受到外生事件和结构性转变的驱动，这使得仅基于历史数值观测的传统预测方法变得不足。虽然语言模型能够检索外部新闻，但标准的检索增强方法在处理高噪声、信号缺失以及无法对事件影响进行因果推理方面存在困难。我们提出了SEER（自演化事件推理与检索），一个闭环框架，能够动态优化时间序列预测的事件条件。SEER将预测误差转化为两种解耦的反馈机制：（i）一个反思性检索记忆，用于优化后续搜索查询并过滤虚假噪声；（ii）一个持久化的因果知识库，用于提炼可迁移的领域动态。SEER在事件检索和反思过程中都严格执行时间顺序边界，防止前视偏差和数据泄漏。在六个波动性时间序列基准测试中，SEER持续优于最先进的时间序列基础模型和语言模型基线。
英文摘要
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-loop framework that dynamically optimizes event conditioning for time series forecasting. SEER translates prediction errors into two decoupled feedback mechanisms: (i) a reflective retrieval memory that refines subsequent search queries and filters spurious noise, and (ii) a persistent causal knowledge base that distills transferable domain dynamics. SEER enforces strict chronological boundaries across both event retrieval and reflection, preventing 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.
发表机构
- Google Cloud AI Research（谷歌云人工智能研究院）
- University of Virginia（弗吉尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
