AI 中文总结
研究时间序列分类，提出MASHT方法，结合MultiRocket和Hydra特征与预训练表格基础模型，绕过特定任务模型训练，在单变量任务中匹配最先进基线，多变量数据集上竞争力强。
AI 中文摘要
时间序列分类在医学信号分析、工业监测和基于传感器的活动识别等领域至关重要，类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。随机卷积变换能将序列高效映射到固定维度表格特征，但传统上与简单线性分类器结合。本文研究预训练表格基础模型能否更有效地利用这些丰富表示。提出MASHT，将MultiRocket和Hydra特征与上下文表格基础模型相结合。通过利用预训练表格基础模型，该方法完全绕过特定任务模型训练，仅需特征提取和直接推理。大量实验表明，MASHT在单变量任务上与最先进的时间序列分类基线匹配，平均排名低于HIVE-COTE 2.0。在多变量数据集上，MASHT与最强参考方法竞争激烈。
英文摘要
Time series classification is central to domains such as 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 capture these diverse patterns by converting time series into rich, fixed-dimensional feature representations that can be processed by standard tabular classifiers. While these representations are traditionally paired with simple linear models, we investigate whether a pretrained tabular foundation model can exploit them more effectively and how its performance depends on the available data and inference budget. We propose MASHT, a pipeline that combines MultiRocket and Hydra features with an in-context tabular foundation model. Our approach uses a pretrained tabular foundation model to bypass 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. Controlled resource experiments show that compact feature tables retain most of the accuracy at substantially lower runtime, while TabPFN outperforms a matched linear baseline across the evaluated label budgets on univariate tasks. These results highlight practical trade-offs between predictive performance, labeled data, and inference cost.
