Arjun Ashok, Mihir Parmar, Palash Goyal, Bhavana Dalvi Mishra, Chun-Liang Li, Irina Rish, Alexandre Drouin, Jinsung Yoon · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26582 · open-access PDF
最近的时间序列基础模型(TSFMs)可以在背景中消耗可变性,将零射预测扩展到由外部因素驱动的问题。这使场景分析成为可能:预测目标如何对设置可变性为未来的值的事件做出反应。他们是否能够可靠地做到这一点是一个开放的问题。我们引入了WhatIf-Bench,一个衡量预测员如何使用可变性来预测目标对未来的事件的反应的基准。
Machine translation (Meta M2M100); the English original follows.
Recent time series foundation models (TSFMs) can consume covariates in-context, extending zero-shot forecasting to problems driven by external factors. This enables scenario analysis: forecasting how a target responds to events that set covariates to chosen future values. Whether they can do this reliably is an open question. We introduce WhatIf-Bench, a benchmark measuring how well forecasters use covariates to forecast a target's response to future events. WhatIf-Bench is semi-synthetic: time series come from mechanistic simulators of data centers, power grids, road traffic, and water networ
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