{"slug": "tracing-the-evidence-behind-zero-shot-time-series-forecasting-a-source-first-and", "title": "Tracing the Evidence Behind Zero-Shot Time-Series Forecasting: A Source-First Taxonomy and Audit Framework", "summary": "A new arXiv paper (2609.21425v1) argues that zero-shot time-series forecasting should be governed as an evidence-access claim rather than a training-status condition, since a frozen language model prompted with serialized values, a time-series model pretrained on broad forecasting corpora, and a retrieval-augmented forecaster can all satisfy the no-update condition while drawing on different transferable evidence. The paper proposes a source-first taxonomy separating three primary evidence sources — frozen LLM prior reuse, parametric time-series pretraining, and retrieval-augmented external memory — from the architectures that implement them, plus four audit questions covering task interface, forecast object and scoring, prediction-time context, and resource budget. The authors' stated agenda is to make zero-shot leaderboards auditable by reporting evidence boundaries and interface assumptions alongside scores so benchmark progress reflects transferable forecasting capability rather than undisclosed changes in context, memory, or budget.", "body_md": "arXiv:2609.21425v1 Announce Type: new \nAbstract: 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.", "url": "https://wpnews.pro/news/tracing-the-evidence-behind-zero-shot-time-series-forecasting-a-source-first-and", "canonical_source": "https://www.machinebrief.com/news/tracing-the-evidence-behind-zero-shot-time-series-forecastin-wvbi", "published_at": "2026-09-21 04:00:00+00:00", "updated_at": "2026-09-21 04:25:20.898438+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "large-language-models"], "entities": ["arXiv", "2609.21425v1"], "alternates": {"html": "https://wpnews.pro/news/tracing-the-evidence-behind-zero-shot-time-series-forecasting-a-source-first-and", "markdown": "https://wpnews.pro/news/tracing-the-evidence-behind-zero-shot-time-series-forecasting-a-source-first-and.md", "text": "https://wpnews.pro/news/tracing-the-evidence-behind-zero-shot-time-series-forecasting-a-source-first-and.txt", "jsonld": "https://wpnews.pro/news/tracing-the-evidence-behind-zero-shot-time-series-forecasting-a-source-first-and.jsonld"}}