In-Context Time Series Classification with Random Convolutional Features

• URL: http://arxiv.org/abs/2607.19234v1
• Date: Tue, 21 Jul 2026 16:04:35 GMT
• Title: In-Context Time Series Classification with Random Convolutional Features
• Abstract summary: Time series classification is central to domains like medical signal analysis, industrial monitoring, and sensor-based activity recognition.<n>We propose MASHT, a pipeline that marries MultiRocket and Hydra features with the power of in-context tabular foundation models.<n>Our approach completely bypasses task-specific model training, requiring only feature extraction and direct inference.
• Score: 38.40148298084127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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