时间索引基础模型实现零样本填补，无需微调即可处理多种数据场景。
Are Time-Indexed Foundation Models the Future of Time Series Imputation?
- 基于时间索引的通用模型，支持跨域零样本填补。
- 在33个外部数据集上验证，覆盖约130万次填补窗口。
- 推理时可直接融合协变量，无需微调提升精度。
时间序列填补的基础模型研究仍处于起步阶段。近期出现的TabPFN-TS和MoTM模型共享同一理念，属于时间索引基础模型。本文首次开展大规模实证研究，评估这些模型在零样本填补中的表现，即无需重训练即可恢复缺失值。我们在33个跨域数据集上进行广泛单变量实验（约130万次填补窗口），并测试其在推理时集成协变量以提升精度的能力，而无需微调。结果表明，时间索引基础模型是实现真实世界时间序列通用、零样本填补的重要且实用的进展。
原文摘要 · Abstract (English)
Foundation models for time series imputation remain largely unexplored. Recently, two such models, TabPFN-TS and MoTM, have emerged. These models share a common philosophy that places them within the family of time-indexed foundation models. This paper presents the first large-scale empirical study of these models for zero-shot imputation, which enables missing value recovery without retraining across a wide range of scenarios. We conduct extensive univariate experiments across 33 out-of-domain datasets (approximately 1.3M imputation windows) and evaluate their ability to integrate covariates at inference time to improve accuracy without fine-tuning. Our results demonstrate that time-indexed foundation models are a powerful and practical step toward achieving general-purpose, zero-shot imputation for real-world time series.
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