用因果图生成带时序结构的合成时间序列数据集，提升分类模型性能。
A Causal DAG Prior for Synthetic Time-Series Classification Datasets
- 基于随机有向无环图构建跨模态因果结构，生成多变量时序数据。
- 在75个UCR/UEA数据集上微调后，模型性能显著优于基线（p=3.0×10⁻⁸）。
- 适合需要真实时序结构的合成数据研究者或时间序列分类任务开发者。
一种先验-数据拟合网络可学习其训练先验所诱导的后验预测；将该范式应用于多变量时间序列分类，需构造能生成完整标注数据集的合成生成器，具备时序结构。本文提出一种因果先验，从跨两种模态（表格属性与时间序列）的类型化节点中随机采样有向无环图（DAG），原生生成具有跨通道、跨时间步和标签间因果结构的多变量、多类别时间序列分类数据集，填补了现有合成先验在该领域的空白。为验证该先验，我们在少量调整下对TabPFN v2.5进行微调，并在TabPFN运行范围内的75个UCR/UEA数据集上评估。在该生成器上微调显著优于原始未修改模型及仅基于表格的消融版本（威尔科克森符号秩检验ROC-AUC p=3.0×10⁻⁸），验证了跨模态时序结构的贡献。
原文摘要 · Abstract (English)
A Prior-data fitted Network learns the posterior predictive induced by its training prior; bringing this paradigm to multivariate time-series classification therefore calls for a synthetic generator that produces complete labelled datasets with temporal structure. We introduce a causal prior that synthesizes each dataset from a randomly sampled DAG over typed nodes across two modalities (tabular attributes and time series), natively producing multivariate, multi-class TSC datasets with cross-modal causal structure across channels, timesteps and labels, a regime not addressed by existing synthetic priors. To validate the prior, we finetune TabPFN v2.5 with minimal adaptations and evaluate on 75 UCR/UEA datasets within TabPFN's operating regime. Finetuning on our generator significantly outperforms both the unmodified upstream model and a tabular-only ablation of the same prior (Wilcoxon signed-rank $p=3.0\times 10^{-8}$ on ROC-AUC), isolating the contribution of the cross-modal temporal structure.
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