arXiv Machine Learning July 22, 2026 By Joscha C\"uppers, Jilles Vreeken

In-Context Time Series Classification with Random Convolutional Features

Read the original on arXiv Machine Learning →

arXiv:2607. 19234v1 Announce Type: new 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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TS2TabPFN: Time Series Classification and Extrinsic Regression through Feature Extraction and a Tabular Foundation Model

Time series data are ubiquitous in practical applications, where classification (TSC) and extrinsic regression (TSER) have emerged as essential tasks for obtaining value from temporal sequences. While the literature has seen significant progress through feature-based and deep learning models, existing methods often focus either on the quality of feature extraction or on the intrinsic predictive power of complex architectures applied to raw data.

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