Trio: 基于时间-空间-样本注意力与结构因果先验的时间序列预测学习
Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors
AI总结：
提出Trio架构，通过时间、空间和样本三种注意力机制分别捕获时序动态、变量间依赖和历史样本对应，并引入时间序列结构因果模型生成合成任务以提供结构先验，提升多变量时间序列预测性能。
摘要：
多变量时间序列预测要求模型对时间动态、跨变量依赖以及历史输入-输出对应关系进行推理。最近的先验数据拟合网络（PFNs）表明，合成任务可用于学习可迁移的推理行为。然而，直接将这一范式迁移到时间序列预测仍然困难，因为时间顺序、动态滞后和重复的历史模式无法被普通的表格先验自然捕获。受此观察启发，我们提出了Trio，一种基于时间-空间-样本注意力的样本感知时间序列预测架构。时间注意力捕获窗口内动态，空间注意力建模变量间依赖，样本注意力检索相关的历史回溯-未来对以指导当前预测。我们的目标并非声称一个完全通用的PFN风格预测器，而是研究如何在预测模型中显式组织和重用历史输入-输出示例。我们进一步引入了一个时间序列结构因果模型（TS-SCM）生成器，以创建具有动态滞后、跨变量交互、噪声、反馈和分布漂移的结构化合成预测任务。在合成、工业和公共基准上的实验表明，所提出的架构提高了预测性能。探索性的零样本实验进一步表明，TS-SCM生成的任务可能提供有用的结构先验，而完全通用的PFN风格时间序列预测仍是一个开放问题。
英文摘要：
Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspondences. Recent Prior-Data Fitted Networks (PFNs) suggest that synthetic tasks can be useful for learning transferable inference behavior. However, directly transferring this paradigm to time-series forecasting remains difficult, since temporal order, dynamic lags, and recurring historical patterns are not naturally captured by ordinary tabular priors. Motivated by this observation, we propose Trio, a sample-aware time-series forecasting architecture based on Temporal-Spatial-Sample attention. Temporal attention captures within-window dynamics, spatial attention models inter-variable dependencies, and sample attention retrieves relevant historical lookback-future pairs to guide the current prediction. Rather than claiming a fully general PFN-style forecaster, our goal is to study how historical input-output examples can be explicitly organized and reused within a forecasting model. We further introduce a Time-Series Structural Causal Model (TS-SCM) generator to create structured synthetic forecasting tasks with dynamic lags, cross-variable interactions, noise, feedback, and distributional drift. Experiments on synthetic, industrial, and public benchmarks show that the proposed architecture improves forecasting performance. Exploratory zero-shot experiments further suggest that TS-SCM-generated tasks may provide useful structural priors, while fully general PFN-style time-series forecasting remains an open problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
