用预训练表格式模型提升随机卷积特征的时序分类效果
In-Context Time Series Classification with Random Convolutional Features
- 结合MultiRocket与Hydra特征，用预训练表格式模型直接推理
- 单变量任务上性能接近最优基线，平均排名更优
- 无需微调，适合快速部署于医疗、工业等场景
时序分类在医疗信号分析、工业监控和基于传感器的行为识别等领域至关重要，其中类别信息表现为局部形状、特定频率、时间偏移或复杂的跨通道交互。随机卷积变换能高效将序列映射为固定维度的表格特征，但传统上仅搭配简单线性分类器使用。本文探究预训练的表格基础模型是否能更有效地利用这些丰富表征。提出MASHT框架，将MultiRocket与Hydra特征与上下文感知的表格基础模型结合。通过使用预训练表格模型，该方法完全跳过任务特定模型训练，仅需特征提取与直接推理。大量实验表明，MASHT在单变量任务上达到与现有最优基线相当的性能，平均排名优于HIVE-COTE 2.0；在多变量数据集上也保持高度竞争力。
原文摘要 · Abstract (English)
Time series classification is central to domains like medical signal analysis, industrial monitoring, and sensor-based activity recognition, where class information manifests as localized shapes, specific frequencies, temporal shifts, or complex cross-channel interactions. Random convolutional transforms efficiently map these sequences to fixed-dimensional tabular features but are traditionally paired with simple linear classifiers. We investigate whether a pretrained tabular foundation model can more effectively harness these rich representations. We propose MASHT, a pipeline that marries MultiRocket and Hydra features with the power of in-context tabular foundation models. By leveraging a pretrained tabular foundation model, our approach completely bypasses task-specific model training, requiring only feature extraction and direct inference. Extensive experiments demonstrate that MASHT matches state-of-the-art time series classification baselines on univariate tasks, achieving a lower average rank than HIVE-COTE 2.0. On multivariate datasets, MASHT remains highly competitive with the strongest reference methods.
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