零样本时间序列预测的证据溯源：一种以来源为先的分类法与审计框架
Tracing the Evidence Behind Zero-Shot Time-Series Forecasting: A Source-First Taxonomy and Audit Framework
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中文总结 AI 辅助
本文提出以来源为先的分类法与审计框架，将零样本时间序列预测视为证据访问声明，区分三种证据来源并明确审计问题，以提升基准可审计性。
中文摘要 AI 辅助
零样本时间序列预测（TSF）常被描述为无需针对目标进行参数更新的预测，但这种训练状态条件并未规定系统可以使用哪些证据。一个使用序列化值提示的冻结语言模型、一个在广泛预测语料库上预训练的时间序列模型，以及一个检索增强的预测器，都可能满足不更新参数的条件，同时利用不同的可迁移证据。本文主张，零样本TSF因此应被视为一种证据访问声明来管理。我们提出了一种以来源为先的分类法，将三种主要证据来源——冻结大语言模型先验复用、参数化时间序列预训练和检索增强外部记忆——与实现它们的架构分离开来。在确定来源之后，仍有四个额外的审计问题：任务接口、预测对象与评分、预测时上下文以及资源预算。由此产生的议程是，通过报告证据边界和接口假设以及得分，使零样本排行榜可审计，从而使基准进展反映可迁移的预测能力，而非未披露的上下文、记忆或预算变化。
英文摘要
Zero-shot time-series forecasting (TSF) is often described as forecasting without target-specific parameter updates, but that training-status condition does not specify what evidence the system may use. A frozen language model prompted with serialized values, a time-series model pretrained on broad forecasting corpora, and a retrieval-augmented forecaster may all satisfy the no-update condition while drawing on different transferable evidence. This paper argues that zero-shot TSF should therefore be governed as an evidence-access claim. We propose a source-first taxonomy that separates three primary evidence sources---frozen LLM prior reuse, parametric time-series pretraining, and retrieval-augmented external memory---from the architectures that implement them. After the source is identified, four additional audit questions remain: task interface, forecast object and scoring, prediction-time context, and resource budget. The resulting agenda is to make zero-shot leaderboards auditable by reporting evidence boundaries and interface assumptions alongside scores, so that benchmark progress reflects transferable forecasting capability rather than undisclosed changes in context, memory, or budget.
发表机构
- Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。
