想不训练模型就做时间序列分类？试试MASHT，把MultiRocket特征丢给表格基础模型，单变量任务已经干掉HIVE-COTE 2.0了。
本文提出MASHT方法，将MultiRocket和Hydra随机卷积特征与预训练表格基础模型结合，完全跳过任务特定训练。在单变量时间序列分类上，MASHT的平均排名低于HIVE-COTE 2.0。在多变量数据集上，MASHT与最强基准方法竞争激烈。实验表明该方法无需训练即可匹配领先结果。
In-Context Time Series Classification with Random Convolutional Features
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.