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.

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