用结构化特征+表格大模型，提升时序分类与外生回归性能
TS2TabPFN: Time Series Classification and Extrinsic Regression through Feature Extraction and a Tabular Foundation Model
- 先提取时序特征，再用TabPFN 2.5模型预测
- 在TSER任务上显著超越现有最佳模型
- 适合需要高精度的时序分析场景
时间序列数据广泛存在于实际应用中，时序分类（TSC）和外生回归（TSER）是挖掘时间序列价值的关键任务。尽管基于特征的方法和深度学习模型已取得显著进展，但现有方法通常只关注特征提取质量或端到端复杂架构的内在预测能力，导致特征工程的可控性与自动化模型性能之间存在断层。本文提出TS2TabPFN框架，将显式特征提取与最新的表格基础模型TabPFN 2.5相结合，以利用其强大的预测能力。大规模实验表明，TS2TabPFN在TSER任务上显著优于当前最先进模型（具有统计显著性），为TSC提供了稳健高效的替代方案，并超越了大多数现有最优算法。结果表明，结合基础模型与结构化特征可突破单一范式局限，建立新的时序分析基准。
原文摘要 · Abstract (English)
Time series data are ubiquitous in practical applications, where classification (TSC) and extrinsic regression (TSER) have emerged as essential tasks for obtaining value from temporal sequences. While the literature has seen significant progress through feature-based and deep learning models, existing methods often focus either on the quality of feature extraction or on the intrinsic predictive power of complex architectures applied to raw data. This division creates a gap between the control offered by feature engineering and the automated performance of end-to-end models. This paper proposes TS2TabPFN, a framework that bridges this gap by integrating explicit feature extraction with TabPFN 2.5, a cutting-edge foundation model for tabular data, to leverage its predictive capabilities. Our extensive experimental evaluation demonstrates that TS2TabPFN significantly outperforms state-of-the-art models in TSER tasks with statistical significance, providing a robust and efficient alternative for TSC and surpassing most of the currently best-performing algorithms. These results suggest that combining foundation models with structured features overcomes single-paradigm limitations, establishing a new time series state-of-the-art.
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