Tracing the Evidence Behind Zero-Shot Time-Series Forecasting: A Source-First Taxonomy and Audit Framework

2026-09-18

AI Summary

本文提出了一种基于证据来源的分类法和审计框架，用于解决零样本时间序列预测中的证据使用问题，明确了三种主要证据来源及其实施架构。

Abstract

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.

Problem

• zero-shot time-series forecasting
• evidence-access claim
• source-first taxonomy

Innovation

• zero-shot time-series forecasting
• evidence-access claim
• source-first taxonomy
• frozen LLM prior reuse
• retrieval-augmented external memory